Chris Callison-Burch: CV

(Last updated March 14, 2023)

Employment

Visiting Research Scientist Allen Institute for Artificial Intelligence (AI2), Seattle, WA	January 2023-
Associate Professor University of Pennsylvania, Philadelphia, PA	June 2017-present
Part-time Visiting Researcher Google (via Adecco), New York, NY	December 2018-December 2020
Aravind K. Joshi Term Assistant Professor University of Pennsylvania, Philadelphia, PA	September 2013-June 2017
Associate Research Professor Johns Hopkins University, Baltimore, MD	June 2010-August 2013
Assistant Research Professor Johns Hopkins University, Baltimore, MD	June 2007-June 2010
Lead of Machine Translation Research Division JHU Human Language Technology Center of Excellence (HLTCOE), Baltimore, MD	May 2010-August 2013
Founder / Director Linear B, Ltd., Edinburgh, UK	October 2002-January 2009
Computational Linguist / Software Engineer Amikai, Inc., San Francisco, CA	June 2000-September 2001

Education

PhD in Informatics University of Edinburgh, Edinburgh, UK Thesis:Paraphrasing and Translation. Advisors:Miles Osborne and Mark Steedman.	February 2008
M.S. with Distinction in Computer Science University of Edinburgh, Edinburgh, UK Thesis:Co-Training for Statistical Machine Translation. Advisors:Miles Osborne.	October 2002
B.S. with Honors in Symbolic Systems Stanford University, Palo Alto, CA Thesis:A Computer Model of a Grammar for English Questions. Advisors:Ivan Sag.	June 2000

Teaching Reviews at Penn

You can read my full teaching reviews here. Below are the summary statistics.

Quality scale (0-4): 0=Poor, 1=Fair, 2=Good, 3=Very Good, 4=Excellent

Term	Course Title (Number)	Students Enrolled	Course Quality	Instructor Quality
Fall 2022	Artificial Intelligence (CIS 4210/5210 - on campus)	363	3.3	3.5
Fall 2022	Artificial Intelligence (CIS 5210 - MCIT Online)	94	3.4	3.6
Fall 2022	Research Practicum (CIS 8000)	16	3.5	3.6
Summer 2022	Artificial Intelligence (CIS 521 - MCIT Online)	70	3.5	3.7
Spring 2022	Interactive Fiction and Text Generation (CIS 700-001)	53	3.3	3.5
Fall 2021	Artificial Intelligence (CIS 421/521 - on campus + MCIT Online)	563	3.2	3.4
Summer 2021	Artificial Intelligence (CIS 521 - MCIT Online)	49	3.0	3.6
Spring 2021	Crowdsourcing and Human Computation (NETS 213)	146	3.0	3.3
Fall 2020	Artificial Intelligence (CIS 421/521)	197	3.1	3.3
Spring 2020	Computational Linguistics (CIS 530)	125	3.3	3.3
Spring 2020	Interactive Fiction and Text Generation (CIS 700-008)	23	3.1	3.3
Fall 2019	Artificial Intelligence (CIS 421/521)	148	3.1	3.3
Summer 2019	Artificial Intelligence (CIS 421/521)	36	2.9	3.0
Spring 2019	Computational Linguistics (CIS 530)	75	2.8	3.0
Spring 2019	Crowdsourcing and Human Computation (NETS 213)	59	2.5	2.7
Fall 2018	Artificial Intelligence (CIS 421/521)	101	2.5	2.5
Spring 2018	Computational Linguistics (CIS 530)	64	2.8	2.7
Fall 2017	Data Structures and Algorithms (CIS 121)	216	2.1	1.7
Fall 2016	Data Structures and Algorithms (CIS 121)	219	2.5	2.2
Spring 2016	Crowdsourcing and Human Computation (NETS 213)	113	2.4	2.8
Fall 2015	Data Structures and Algorithms (CIS 121)	174	2.2	2.2
Spring 2015	Machine Translation (CIS 526)	51	2.9	3.2
Fall 2014	Crowdsourcing and Human Computation (NETS 213)	48	3.2	3.6
Spring 2014	Machine Translation (CIS 526)	25	3.3	3.5
Fall 2013	Crowdsourcing and Human Computation (CIS 399)	26	3.1	3.5

Grants

Current grants

Grant Title	Awarding Body	Amount	Dates	PI Info
IARPA HIATUS: PAUSIT: Privacy protection and Authorship attribution Using Style-based Interpretable Transfer	IARPA	$7m	2022-2026	Chris Callison-Burch - PI, Marianna Apidianaki - co-PI, Kathleen McKeown (Columbia), Smaranda Muresan (Columbia), Owen Rambow (Stony Brook University), Niranjan Balasubramanian (Stony Brook University), Andy Schwartz (Stony Brook University)
Computing Innovation Postdoctoral Fellow Award	NSF/CRA	~$140k	2021-2023	Lara Martin (postdoc), Chris Callison-Burch (faculty advisor)
FFW-HTF-RL: Collaborative Research: Up-skilling and Re-skilling Marginalized Rural and Urban Digital Workers: AI-worker collaboration to access creative work	NSF	$3m	2019-2024	Jeffrey Bigham (CMU) - PI, Chris Callison-Burch (UPenn), Ben Hanrahan (Penn State), Niki Kittur (CMU), Beibei Li (CMU), Amy Ogan (CMU), Amy Pavel (CMU), Saiph Savage (West Virgina University), Julia Ticona (UPenn)
LWLL: FLASH: Fast Learning via Auxiliary signals, Structured knowledge, and Human expertise	DARPA	$3.3m	2019-2022	Dan Roth (PI - UPenn), Irfan Essa (Georgia Tech), Chris Callison-Burch (UPenn), Zsolt Kira (Georgia Tech), Le Song (Georgia Tech), Mayur Naik (UPenn), Osbert Bastani (UPenn)
KAIROS: RESIN: Reasoning about Event Schemas for Induction of kNowledge	DARPA	$12m	2019-2024	Heng Ji (PI - UIUC), Mohit Bansal (UNC), Chris Callison-Burch (UPenn), Shih-Fu Chang (Columbia), Jiawei Han (UIUC), Martha Palmer (Colorado), Dan Roth (UPenn), Carl Vondrick (Columbia)
BETTER: Task and User-Aware Representation Learning for Fine-Grained Cross-Lingual Information Retrieval	IARPA	$6m	2019-2023	Ellie Pavlick (PI - Brown), Carsten Eickhoff (Brown), Chris Callison-Burch (UPenn), Wei Xu (OSU), Alan Ritter (OSU)

Pending grants

Grant Title	Awarding Body	Amount	Dates	PI Info

Past grants

Grant Title	Awarding Body	Amount	Dates	PI Info
Semi-supervised Learning of Multimodal Representations	DARPA	$428k	2019-2022	Chris Callison-Burch (PI-UPenn) and Derry Wijaya (Boston University)
STTR: Personalized Retrieval-based Simplification	NSF	$225k	2019-2021	Eleni Miltsakaki (Choosito.com) - PI, Chris Callison-Burch
Alexa Prize TaskBot Challenge	Amazon	$250k	2021-2022	Chris Callison-Burch (PI), Mark Yatskar (UPenn)
REU Supplement	NSF	$16k	2019-2020	co-PI Chris Cieri
SPUR WOMEN: Support and Promote Undergraduate Research for Women	Google	$15k from Google	2019-2020	PI with Rita Powell
Google Faculty Research Award (Using Self-Identification of Group Membership to Explore Differences in Argumentation and Evidence Finding)	Google	$80,000	2019	Chris Callison-Burch and Emily Falk
SPUR WOMEN: Support and Promote Undergraduate Research for Women	Google	$25k from Google, plus $25k matched by SEAS	2018-2019	PI with Rita Powell
CI-NEW: NIEUW: Novel Incentives and Workflows in Linguistics Data Collection	NSF	$1.2m	2017-2022	co-PI with Christopher Cieri and Mark Liberman
DEFT Extension	DARPA	$116,000	2017-2017	PI with Ben Van Durme
CI-P: Planning for Scalable Language Resource Creation through Novel Incentives and Crowdsourcing	NSF	$100,000	2016-2017	co-PI with Christopher Cieri and Mark Liberman
Amazon Web Services supplimental grant to Amazon Academic Research Award	Amazon	$40,000	2016-2017	PI
Low Resource Machine Translation via Matrix Factorization (Amazon Academic Research Awards)	Amazon	$68,000	2016-2017	PI
Learning translations from monolingual texts (LORELEI)	DARPA	$478,000	2015-2019	PI at Penn
SIREN-IL: Specialized Intra/Interlingual Resources for Emergent News - Incident Language	DARPA	$3,031,412	2015-2017	co-PI with Stephanie Strassel
Natural Logic Solver for Aristo	Allen Institute for Artificial Intelligence (AI2)	$95,000	2015-2016	PI
EAGER: Simplification as Machine Translation	NSF	$100,000	2014-2015	PI
Unsolicited Gift	Facebook	$50,000	2014
Sloan Research Fellowship	Alfred P. Sloan Foundation	$50,000	2014
Google Faculty Research Award (Learning Paraphrases from Large, Diverse Data Sets)	Google	$62,000	2013
Large-scale Paraphrasing for Natural Language Understanding (DEFT)	DARPA	$1,600,000	2012-2017	PI with Ben Van Durme
EAGER: Combining natural language inference and data-driven paraphrasing	NSF	$100,000	2012-2013	co-PI with Ben Van Durme
Crowdsourcing Translation (Computer Science Study Panel phase 3)	DARPA	$500,000	2012-2015	PI
Improved Arabic dialect translation through Crowdsourcing	DARPA	$176,000	2012-2013	PI
Acquisition and use of paraphrases in a knowledge-rich setting	Vulcan	$260,000	2011-2013	co-PI with Ben Van Durme
Google Faculty Research Award (Translate the World: A Unified Framework for Crowdsourcing Translation)	Google	$150,000	2011	co-PI with Philip Resnik and Ben Bederson
Crowdsourcing Translation	Microsoft	$25,000	2011
RI:Medium: Semi-supervised Discriminative Training of Sequence Transduction Model	NSF	$800,000	2011-2015	co-PI with Sanjeev Khudanpur, Brian Roark, Damianos Karakos, Richard Sproat
Translation of informal texts via Mechanical Turk	BBN Technologies	$144,000	2010-2011	PI
BABEL: Bayesian Architecture Begetting Every Language (Computer Science Study Panel phase 2)	DARPA	$500,000	2010-2012	PI
Google Faculty Research Award (The Babel Challenge: Translating all the World’s Languages)	Google	$45,000	2010	co-PI with Miles Osborne
EuroMatrixPlus: Bringing Machine Translation for European Languages to the User	European Union Framework 7 Programme	€4,950,000	2009-2012	PI at JHU (JHU Amount: €516,000)
Global Autonomous Language Exploitation (GALE) project, Periods 3 and 4	DARPA	$575,000	2009-2011	co-PI with Sanjeev Khudanpur and Damianos Karakos
Computer Science Study Panel	DARPA	$93,000	2008-2009	PI
RI:SMALL: Multi-Level Modeling of Language and Translation	NSF	$400,000	2007-2012	co-PI with David Yarowsky
SMART:Scotland Technology Innovation Grant	British Government	£45,000	2002-2005

Publications

Refereed conference papers (most have acceptance rates ≈ 25%)

1. Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, Mark Yatskar (2023). Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification. CVPR 2023. 18 pages.
2. Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Sherry Shi, Chris Callison-Burch (2023). Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text. AAAI 2023. 13 pages.
3. Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, Chris Callison-Burch (2023). Causal Reasoning About Entities and Events in Procedural Texts. Findings of EACL 2023.
4. Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch (2023). Bidirectional Language Models Are Also Few-shot Learners. ICLR 2023. 25 pages.
5. Chris Callison-Burch, Gaurav Singh Tomar, Lara Martin, Daphne Ippolito, Suma Bailis and David Reitter (2022). Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence. EMNLP 2022. 15 pages.
6. Jeffrey Young-Min Cho, Harry Li Zhang, Chris Callison-Burch (2022). Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection. EMNLP 2022. 9 pages.
7. Yue Yang, Artemis Panagopoulou, Marianna Apidianaki, Mark Yatskar and Chris Callison-Burch (2022). Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction. Findings of EMNLP 2022. 17 pages.
8. Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch (2022). The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank. NAACL 2022. 12 pages.
9. Qing Lyu, Hua Zheng, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch (2022). Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases. NAACL 2022. 12 pages.
10. Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, Jason Wei (2022). A Recipe For Arbitrary Text Style Transfer with Large Language Models. ACL 2022. 12 pages.
11. Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini (2022). Deduplicating Training Data Makes Language Models Better. ACL 2022. 20 pages.
12. Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig (2022). Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data. ACL 2022. 14 pages.
13. Liam Dugan, Eleni Miltsakaki, Etan Ginsberg, Shriyash Upadhyay, Hannah Gonzalez, Dahyeon Choi, Chuning Yuan, Chris Callison-Burch (2022). A Feasibility Study of Answer-Agnostic Question Generation for Education. ACL 2022.
14. Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, and Sebastian Gehrmann (2021). SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets. NeurIPS 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks. 2021. 24 pages.
15. Qing Lyu and Li Zhang and Chris Callison-Burch (2021). Goal-Oriented Script Construction. INGL 2021. 17 pages.
16. Nikzad Khani, Isidora Chara Tourni, Mohammad Sadegh Rasooli, Chris Callison-Burch and Derry Tanti Wijaya (2021). Cultural and Geographical Influences on Image Translatability of Words across Languages. NAACL 2021. 12 pages.
17. Qing Lyu*, Li Zhang*, Chris Callison-Burch (2020). Reasoning about Goals, Steps, and Temporal Ordering with WikiHow. EMNLP 2020. 11 pages.
18. Li Zhang, Qing Lyu, Chris Callison-Burch (2020). Intent Detection with WikiHow. AACL-IJCNLP 2020. 6 pages.
19. Daphne Ippolito*, Daniel Duckworth*, Chris Callison-Burch and Douglas Eck (2020). Automatic Detection of Generated Text is Easiest when Humans are Fooled. ACL 2020. 15 pages.
20. Daphne Ippolito, David Grangier, Douglas Eck and Chris Callison-Burch (2020). Toward Better Storylines with Sentence-Level Language Models. ACL 2020. Short papers. 7 pages.
21. Daphne Ippolito*, Reno Kriz*, João Sedoc, Maria Kustikova and Chris Callison-Burch (2019). Comparison of Diverse Decoding Methods from Conditional Language Models. ACL 2019. 11 pages.
22. Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, and Chris Callison-Burch (2019). Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification. NAACL 2019. 10 pages.
23. Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch and Dan Roth (2019). Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims. NAACL 2019. 16 pages.
24. Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Benjamin V. Hanrahan, Jeffrey P. Bigham and Chris Callison-Burch (2019). Worker Demographics and Earnings on Amazon Mechanical Turk: An Exploratory Analysis. CHI Late Breaking Work 2019. 5 pages.
25. Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki and Chris Callison-Burch (2018). Learning Scalar Adjective Intensity from Paraphrases. EMNLP 2018. 11 pages.
26. John Hewitt*, Daphne Ippolito*, Brendan Callahan, Reno Kriz, Derry Wijaya and Chris Callison-Burch (2018). Learning Translations via Images with a Massively Multilingual Image Dataset. ACL 2018. 12 pages.
27. Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki and Chris Callison-Burch (2018). Simplification Using Paraphrases and Context-based Lexical Substitution. NAACL 2018. 12 pages.
28. Marianna Apidianaki, Guillaume Wisniewski, Anne Cocos and Chris Callison-Burch (2018). Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation. NAACL 2018. Short papers. 6 pages.
29. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2018). Comparing Constraints for Taxonomic Organization. NAACL 2018. 12 pages.
30. Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham (2018). A Data-Driven Analysis of Workers’ Earnings on Amazon Mechanical Turk. CHI 2018. Honorable Mention Award. 12 pages.
31. Derry Wijaya, Brendan Callahan, John Hewitt, Jie Gao, Xiao Ling, Marianna Apidianaki and Chris Callison-Burch (2017). Learning Translations via Matrix Completion. EMNLP 2017. 12 pages.
32. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2017). Mapping the Paraphrase Database to WordNet. STARSEM 2017.
33. Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki and Vered Shwartz (2017). Learning Antonyms with Paraphrases and a Morphology-aware Neural Network. STARSEM 2017.
34. Ann Cocos and Chris Callison-Burch (2017). The Language of Place: Semantic Value from Geospatial Context. EACL 2017. Short papers. 5 pages.
35. Ellie Pavlick, Heng Ji, Xiaoman Pan and Chris Callison-Burch (2016). The Gun Violence Database: A new task and data set for NLP. EMNLP 2016. Short papers. 6 pages.
36. Ellie Pavlick and Chris Callison-Burch (2016). Tense Manages to Predict Implicative Behavior in Verbs. EMNLP 2016. Short papers. 5 pages.
37. Ellie Pavlick and Chris Callison-Burch (2016). So-Called Non-Subsective Adjectives. STARSEM 2016. Best Paper Award. 6 pages.
38. Ellie Pavlick and Chris Callison-Burch (2016). Most babies are little and most problems are huge: Compositional Entailment in Adjective-Nouns. ACL 2016. 11 pages.
39. Ellie Pavlick and Chris Callison-Burch (2016). Simple PPDB: A Paraphrase Database for Simplification. ACL 2016. Short papers. 6 pages.
40. Anne Cocos and Chris Callison-Burch (2016). Clustering Paraphrases by Word Sense. NAACL 2016. 10 pages.
41. Courtney Napoles, Chris Callison-Burch, and Matt Post (2016). Sentential Paraphrasing as Black-Box Machine Translation. NAACL 2016. Short papers. 5 pages.
42. Ellie Pavlick, Johan Bos, Malvina Nissim, Charley Beller, Benjamin Van Durme, and Chris Callison-Burch (2015). Adding Semantics to Data-Driven Paraphrasing. ACL 2015. 10 pages.
43. Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevich, Ben Van Durme, Chris Callison-Burch (2015). PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. ACL 2015. Short papers. 6 pages.
44. Ellie Pavlick, Juri Ganitkevich, Tsz Ping Chan, Xuchen Yao, Ben Van Durme, Chris Callison-Burch (2015). Domain-Specific Paraphrase Extraction. ACL 2015. Short papers. 6 pages.
45. Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, Chris Callison-Burch, Mark Drezde, Ben Van Durme (2015). FrameNet+: Fast Paraphrastic Tripling of FrameNet. ACL 2015. Short papers. 6 pages.
46. Mingkun Gao, Wei Xu, and Chris Callison-Burch (2015). Cost Optimization for Crowdsourcing Translation. NAACL 2015. 9 pages.
47. Heba Elfardy, Mona Diab and Chris Callison-Burch (2015). Ideological Perspective Detection Using Semantic Features. STARTSEM 2015. 10 pages.
48. Ann Irvine and Chris Callison-Burch (2014). Hallucinating Phrase Translations for Low Resource MT. CoNLL 2014. 11 pages.
49. Rui Yan, Mingkun Gao, Ellie Pavlick, and Chris Callison-Burch (2014). Are Two Heads are Better than One? Crowdsourced Translation via a Two-Step Collaboration between Translators and Editors. ACL 2014. 11 pages.
50. Jonathan Weese, Juri Ganitkevitch, and Chris Callison-Burch (2014). PARADIGM: Paraphrase Diagnostics through Grammar Matching. EACL 2014. 10 pages.
51. Ellie Pavlick, Rui Yan, and Chris Callison-Burch (2014). Crowdsourcing for Grammatical Error Correction. CSCW Poster 2014. 4 pages.
52. Juri Ganitkevitch and Chris Callison-Burch (2014). The Multilingual Paraphrase Database. LREC 2014. 8 pages.
53. Ann Irvine, Joshua Langfus, and Chris Callison-Burch (2014). The American Local News Corpus. LREC 2014. 4 pages.
54. Ryan Cotterell and Chris Callison-Burch (2014). A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic. LREC 2014. 5 pages.
55. Xuchen Yao, Ben Van Durme, Chris Callison-Burch and Peter Clark (2013). Semi-Markov Phrase-based Monolingual Alignment. EMNLP 2013. 11 pages.
56. Xuchen Yao, Peter Clark, Ben Van Durme and Chris Callison-Burch (2013). A Lightweight and High Performance Monolingual Word Aligner. ACL 2013. Short papers. 6 pages.
57. Travis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu and Xuchen Yao (2013). PARMA: A Predicate Argument Aligner. ACL 2013. Short papers. 6 pages.
58. Jason Smith, Herve Saint-Amand, Magdalena Plamada, Philipp Koehn, Chris Callison-Burch and Adam Lopez (2013). Dirt Cheap Web-Scale Parallel Text from the Common Crawl. ACL 2013. 10 pages.
59. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2013). PPDB: The Paraphrase Database. NAACL 2013. Short papers. 7 pages.
60. Ann Irvine and Chris Callison-Burch (2013). Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals. NAACL 2013. Short papers. 6 pages.
61. Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch and Peter Clark (2013). Answer Extraction as Sequence Tagging with Tree Edit Distance. NAACL 2013. 10 pages.
62. Xuchen Yao, Benjamin Van Durme and Chris Callison-Burch (2012). Expectations of Word Sense in Parallel Corpora. NAACL 2012. Short papers. 5 pages.
63. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2012). Monolingual Distributional Similarity for Text-to-Text Generation. STARSEM 2012. 9 pages.
64. Rabih Zbib, Erika Malchiodi, Jacob Devlin, David Stallard, Spyros Matsoukas, Richard Schwartz, John Makhoul, Omar F. Zaidan and Chris Callison-Burch (2012). Machine Translation of Arabic Dialects. NAACL 2012. 11 pages.
65. Alex Klementiev, Ann Irvine, Chris Callison-Burch, and David Yarowsky (2012). Toward Statistical Machine Translation without Parallel Corpora. EACL 2012. 11 pages.
66. Juri Ganitkevitch, Chris Callison-Burch, Courtney Napoles, and Benjamin Van Durme (2011). Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation. EMNLP 2011. 12 pages.
67. Omar Zaidan and Chris Callison-Burch (2011). The Arabic Online Commentary Dataset: An Annotated Dataset of Informal Arabic with High Dialectal Content. ACL 2011. Short papers. 5 pages.
68. Omar Zaidan and Chris Callison-Burch (2011). Crowdsourcing Translation: Professional Quality from Non-Professionals. ACL 2011. 10 pages.
69. Lane Schwartz, Chris Callison-Burch, William Schuler and Stephen Wu (2011). Incremental Syntactic Language Models for Phrase-based Translation. ACL 2011. 12 pages.
70. Omar Zaidan and Chris Callison-Burch (2010). Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators. NAACL 2010. Short papers. 4 pages.
71. Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie Dorr, Scott Miller, Christine Piatko, Nathaniel W. Filardo, and Lori Levin (2010). Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach. AMTA 2010. 10 pages.
72. Ann Irvine, Alex Klementiev, and Chris Callison-Burch (2010). Transliterating From All Languages. AMTA 2010. 8 pages.
73. Michael Bloodgood and Chris Callison-Burch (2010). Large-Scale, Cost-Focused Active Learning for Statistical Machine Translation. ACL 2010. 11 pages.
74. Abby Levenberg, Chris Callison-Burch, and Miles Osborne (2010). Stream-based Translation Models for Statistical Machine Translation. NAACL 2010. 9 pages.
75. Scott Novotney and Chris Callison-Burch (2010). Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription. NAACL 2010. 9 pages.
76. Chris Callison-Burch (2009). Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk. EMNLP 2009. Nominated for the ACL 2019 Test of Time Award. 10 pages.
77. Omar Zaidan and Chris Callison-Burch (2009). Feasibility of Human-in-the-loop Minimum Error Rate Training. EMNLP 2009. 10 pages.
78. Yuval Marton, Chris Callison-Burch and Philip Resnik (2009). Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases. EMNLP 2009. 10 pages.
79. Nikesh Garera, Chris Callison-Burch and David Yarowsky (2009). Improving Translation Lexicon Induction from Monolingual Corpora via Dependency Contexts and Part-of-Speech Equivalences. CoNLL 2009. 9 pages.
80. Chris Callison-Burch (2008). Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. EMNLP 2008. 10 pages.
81. Chris Callison-Burch, Trevor Cohn, Mirella Lapata (2008). ParaMetric: An Automatic Evaluation Metric for Paraphrasing. CoLing 2008. 8 pages.
82. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst (2007). Moses: Open source toolkit for statistical machine translation. ACL 2007.
83. Chris Callison-Burch, Philipp Koehn and Miles Osborne (2006). Improved Statistical Machine Translation Using Paraphrases. NAACL 2006.
84. Chris Callison-Burch, Miles Osborne and Philipp Koehn (2006). Re-evaluating the Role of Bleu in Machine Translation Research. EACL 2006. 8 pages.
85. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). Scaling Phrase-Based Statistical Machine Translation to Larger Corpora and Longer Phrases. ACL 2005.
86. Colin Bannard and Chris Callison-Burch (2005). Paraphrasing with Bilingual Parallel Corpora. ACL 2005.
87. Chris Callison-Burch, David Talbot and Miles Osborne (2004). Statistical Machine Translation with Word- and Sentence-Aligned Parallel Corpora. ACL 2004.
88. Chris Callison-Burch and Raymond Flournoy (2001). A program for automatically selecting the best output from multiple machine translation engines. MT Summit 2001.

Journal articles

1. Aditya Kashyap, Chris Callison-Burch, Mary Regina Boland (2022). A Deep Learning Method to Detect Opioid Prescription and Opioid Use Disorder from Electronic Health Records. International Journal of Medical Informatics 2022. 44 pages.
2. Monique A Sager, Aditya M Kashyap, Mila Tamminga, Sadhana Ravoori, Chris Callison-Burch and Jules B Lipoff (2021). Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR 2021. 7 pages.
3. Isabel Straw and Chris Callison-Burch (2020). Artificial Intelligence in mental health and the biases of language based models. PloS one 2020. 19 pages.
4. Aditya Kashyap, Heather Burris, Chris Callison-Burch, Mary Regina Boland (2020). The CLASSE GATOR (CLinical Acronym SenSE disambiGuATOR): A Method for Predicting Acronym Sense from Neonatal Clinical Notes. International Journal of Medical Informatics 2020.
5. Benjamin Chrisinger, Eliza Kinsey, Ellie Pavlick, Chris Callison-Burch (2020). SNAP judgments into the digital age: Reporting on food stamps varies significantly with time, publication type, and political leaning. PloS one 2020. 19 pages.
6. Anne Cocos and Chris Callison-Burch (2019). Paraphrase-Sense-Tagged Sentences. TACL 2019. 15 pages.
7. Edidiong Okon, Vishnutheja Rachakonda, Hyo Jung Hong, Chris Callison-Burch and Jules Lipoff (2019). Natural Language Processing of Reddit Data to Evaluate Dermatology Patient Experiences and Therapeutics. Journal of the American Academy of Dermatology 2019. 20 pages.
8. Anne Cocos, Ting Qiana, Chris Callison-Burch, and Aaron J. Masino (2017). Crowd Control: Effectively Utilizing Unscreened Crowd Workers for Biomedical Data Annotation. Journal of Biomedical Informatics 2017. 22 pages.
9. Wei Xu, Courtney Napoles, Ellie Pavlick, Jim Chen, and Chris Callison-Burch (2016). Optimizing Statistical Machine Translation for Text Simplification. TACL 2016. 15 pages.
10. Ann Irvine and Chris Callison-Burch (2016). A Comprehensive Analysis of Bilingual Lexicon Induction. Computational Linguistics 2016. 38 pages.
11. Ann Irvine and Chris Callison-Burch (2016). End-to-End Statistical Machine Translation with Zero or Small Parallel Texts. Journal of Natural Language Engineering 2016. 34 pages.
12. Wei Xu, Chris Callison-Burch, and Courtney Napoles (2015). Problems in Current Text Simplification Research: New Data Can Help. TACL 2015. 16 pages.
13. Omar Zaidan and Chris Callison-Burch (2014). Arabic Dialect Identification. Computational Linguistics 2014. 36 pages.
14. Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan and Yangfeng Ji (2014). Extracting Lexically Divergent Paraphrases from Twitter. TACL 2014. 14 pages.
15. Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, and Chris Callison-Burch (2014). The Language Demographics of Amazon Mechanical Turk. TACL 2014. 13 pages.
16. Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, Lin Yang, and Shang Zhao (2013). Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT. TACL 2013. 13 pages.
17. Kathryn Baker, Bonnie Dorr, Michael Bloodgood, Chris Callison-Burch, Wes Filardo, Christine Piatko, Lori Levin, and Scott Miller (2012). Use of Modality and Negation in Semantically-Informed Syntactic MT. Computational Linguistics 2012. 28 pages.
18. Ann Irvine, Mike Kayser, Zhifei Li, Wren Thornton, and Chris Callison-Burch (2010). Integrating Output from Specialized Modules in Machine Translation: Transliteration in Joshua. PBML 2010. 10 pages.
19. Jonathan Weese and Chris Callison-Burch (2010). Visualizing Data Structures in Parsing-Based Machine Translation. PBML 2010. 10 pages.
20. Lane Schwartz and Chris Callison-Burch (2010). Hierarchical Phrase-Based Grammar Extraction in Joshua: Suffix Arrays and Prefix Trees. PBML 2010. 10 pages.
21. Zhifei Li, Chris Callison-Burch, Sanjeev Khudanpur, and Wren Thornton (2009). Decoding in Joshua: Open Source, Parsing-Based Machine Translation. PBML 2009. 10 pages.
22. Trevor Cohn, Chris Callison-Burch, Mirella Lapata (2008). Constructing Corpora for the Development and Evaluation of Paraphrase Systems. Computational Linguistics 2008. 18 pages.

Book chapters

1. Wauter Bosma and Chris Callison-Burch (2007). Paraphrase Substitution for Recognizing Textual Entailment. Evaluation of Multilingual and Multimodalformation Retrieval, Lecture Notes in Computer Science, C Peters et al editors 2007.
2. Chris Callison-Burch and Miles Osborne (2003). Statistical Natural Language Processing. A Handbook for Language Engineers, Ali Farghaly, Editor 2003.

Refereed workshop papers

1. Harry Li Zhang, Chris Callison-Burch (2023). Language Models are Drummers: Drum Composition with Natural Language Pre-Training. AAAI 2023 Workshop on Creative AI Across Modalities 2023. 8 pages.
2. Rebecca Iglesias-Flores, Megha Mishra, Ajay Patel, Akanksha Malhotra, Reno Kriz, Martha Palmer and Chris Callison-Burch (2021). TopGuNN: Fast NLP Training Data Augmentation using Large Corpora. Workshop on Data Science with Human in the Loop 2021. 15 pages.
3. Anietie Andy, Chris Callison-Burch and Derry Wijaya (2020). Resolving Pronouns in Twitter Streams: Context Can Help. Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC) 2020. 6 pages.
4. Anietie Andy, Derry Wijaya and Chris Callison-Burch (2019). Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events. Proceedings of the Second Workshop on Storytelling 2019. 5 pages.
5. Aina Garí Soler, Anne Cocos, Marianna Apidianaki, Chris Callison-Burch (2019). A Comparison of Context-sensitive Models for Lexical Substitution. 13th International Conference on Computational Semantics (IWCS) 2019. 12 pages.
6. Daphne Ippolito, David Grangier, Chris Callison-Burch and Douglas Eck (2019). Unsupervised Hierarchical Story Infilling. 13th International Conference on Computational Semantics (IWCS) 2019. 7 pages.
7. Bhavna Saluja, Gaurav Kumar, João Sedoc, and Chris Callison-Burch (2019). Anonymization of Sensitive Information in Medical Health Records. Iberian Languages Evaluation Forum 2019. 7 pages.
8. Joao Sedoc*, Daphne Ippolito*, Arun Kirubarajan, Jai Thirani, Lyle Ungar, and Chris Callison-Burch (2018). ChatEval: A Tool for the Systematic Evaluation of Chatbots. Workshop on Intelligent Interactive Systems and Language Generation 2018. 4 pages.
9. Anietie Andy, Mark Dredze, Mugizi Rwebangira, and Chris Callison-Burch (2017). Constructing an Alias List for Named Entities During an Event. Workshop on Noisy User-generated Text 2017. 5 pages.
10. Courtney Napoles and Chris Callison-Burch (2017). Systematically Adapting Machine Translation for Grammatical Error Correction. 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA12) 2017. 12 pages.
11. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2017). Word Sense Filtering Improves Embedding-Based Lexical Substitution. Workshop on Sense, Concept and Entity Representations and their Applications 2017. Best Paper Award. 9 pages.
12. Ellie Pavlick and Chris Callison-Burch (2016). The Gun Violence Database. Bloomberg Data for Good Exchange 2016. 6 pages.
13. Wei Xu, Chris Callison-Burch, and Bill Dolan (2015). SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter. SemEval 2015. 11 pages.
14. Anne Cocos, Aaron J. Masino, Ting Qian, Ellie Pavlick, and Chris Callison-Burch (2015). Effectively Crowdsourcing Radiology Report Annotations. Sixth International Workshop on Health Text Mining and Information Analysis 2015. 6 pages.
15. Courtney Napoles and Chris Callison-Burch (2015). Automatically Scoring Freshman Writing: A Preliminary Investigation. Workshop on Innovative Use of NLP for Building Educational Applications 2015. 10 pages.
16. Ellie Pavlick and Chris Callison-Burch (2015). Extracting Structured Information via Automatic + Human Computation. HCOMP 2015. 2 pages.
17. Gaurav Kumar, Yuan Cao, Ryan Cotterell, Chris Callison-Burch, Daniel Povey, and Sanjeev Khudanpur (2014). Translations of the CALLHOME Egyptian Arabic corpus for conversational speech translation. IWSLT 2014. 5 pages.
18. Quanze Chen, Chenyang Lei, Wei Xu, Ellie Pavlick and Chris Callison-Burch (2014). Poetry of the Crowd: A Human Computation Algorithm to Convert Prose into Rhyming Verse. HCOMP Poster 2014. 3 pages.
19. Chris Callison-Burch (2014). Crowd-Workers: Aggregating Information Across Turkers To Help Them Find Higher Paying Work. HCOMP Poster 2014. 2 pages.
20. Ann Irvine and Chris Callison-Burch (2014). Using Comparable Corpora to Adapt MT Models to New Domains. WMT 2014. 8 pages.
21. Ryan Cotterell, Adithya Renduchintala, Naomi Saphra, and Chris Callison-Burch (2014). An Algerian Arabic-French Code-Switched Corpus. LREC Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools 2014. 4 pages.
22. Matt Post, Gaurav Kumar, Adam Lopez, Damianos Karakos, Chris Callison-Burch and Sanjeev Khudanpur (2013). Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus. IWSLT 2013. 7 pages.
23. Ondrej Bojar, Christian Buck, Chris Callison-Burch, Christian Federmann, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia (2013). Findings of the 2013 Workshop on Statistical Machine Translation. WMT 2013. 44 pages.
24. Matt Post, Juri Ganitkevitch, Luke Orland, Jonathan Weese, Yuan Cao, and Chris Callison-Burch (2013). Joshua 5.0: Sparser, better, faster, server. WMT 2013. 7 pages.
25. Ann Irvine and Chris Callison-Burch (2013). Combining Bilingual and Comparable Corpora for Low Resource Machine Translation. WMT 2013. 9 pages.
26. Chris Callison-Burch, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia (2012). Findings of the 2012 Workshop on Statistical Machine Translation. WMT 2012. 42 pages.
27. Matt Post, Chris Callison-Burch, and Miles Osborne (2012). Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing. WMT 2012. 9 pages.
28. Jonathan Weese, Chris Callison-Burch, and Adam Lopez (2012). Using Categorial Grammar to Label Translation Rules. WMT 2012. 10 pages.
29. Juri Ganitkevitch, Yuan Cao, Jonathan Weese, Matt Post, and Chris Callison-Burch (2012). Joshua 4.0: Packing, PRO, and Paraphrases. WMT 2012. 9 pages.
30. Ann Irvine, Jonathan Weese, and Chris Callison-Burch (2012). Processing Informal, Romanized Pakistani Text Messages. the NAACL Workshop on Language in Social Media 2012. 4 pages.
31. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan (2011). Findings of the 2011 Workshop on Statistical Machine Translation. WMT 2011. 43 pages.
32. Charley Chan, Chris Callison-Burch, and Benjamin Van Durme (2011). Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity. GEMS 2011. 10 pages.
33. Jonathan Weese, Juri Ganitkevitch, Chris Callison-Burch, Matt Post and Adam Lopez (2011). Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor. WMT 2011. 7 pages.
34. Byung Gyu Ahn, Ben Van Durme and Chris Callison-Burch (2011). WikiTopics: What is Popular on Wikipedia and Why. ACL Workshop on Automatic Summarization for Different Genres, Media, and Languages 2011. 8 pages.
35. Courtney Napoles, Ben Van Durme (2011). Evaluating sentence compression: Pitfalls and suggested remedies. Workshop on Monolingual Text-To-Text Generation 2011. 7 pages.
36. Courtney Napoles, Chris Callison-Burch, Juri Ganitevitch, Ben Van Durme (2011). Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion. Workshop on Monolingual Text-To-Text Generation 2011. 7 pages.
37. Rui Wang and Chris Callison-Burch (2011). Paraphrase Fragment Extraction from Monolingual Comparable Corpora. BUCC 2011. 9 pages.
38. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Ann Irvine, Lane Schwartz, Wren N. G. Thornton, Ziyuan Wang, Jonathan Weese and Omar F. Zaidan (2010). Joshua 2.0: A Toolkit for Parsing-Based Machine Translationwith Syntax, Semirings, Discriminative Training and Other Goodies. WMT 2010. 5 pages.
39. Chris Callison-Burch, Philipp Koehn, Christof Monz, Kay Peterson, Mark Przybocki, Omar Zaidan (2010). Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation. WMT 2010. 33 pages.
40. Chris Callison-Burch and Mark Dredze (2010). Creating Speech and Language Data With Amazon’s Mechanical Turk. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk 2010. 12 pages.
41. Michael Bloodgood and Chris Callison-Burch (2010). Using Mechanical Turk to Build Machine Translation Evaluation Sets. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk 2010. 4 pages.
42. Scott Novotoney and Chris Callison-Burch (2010). Crowdsourced Accessibility: Elicitation of Wikipedia Articles. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk 2010. 4 pages.
43. Rui Wang and Chris Callison-Burch (2010). Cheap Facts and Counter-Facts. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk 2010. 5 pages.
44. Chris Callison-Burch, Philipp Koehn, Christof Monz and Josh Schroeder (2009). Findings of the 2009 Workshop on Statistical Machine Translation. WMT 2009. 28 pages.
45. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Sanjeev Khudanpur, Lane Schwartz, Wren Thornton, Jonathan Weese and Omar Zaidan (2009). Joshua: An Open Source Toolkit for Parsing-based Machine Translation. WMT 2009. 5 pages.
46. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2008). Further Meta-Evaluation of Machine Translation. WMT 2008. 37 pages.
47. Delip Rao, David Yarowsky, Chris Callison-Burch (2008). Affinity Measures based on the Graph Laplacian. of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing at CoLing 2008. 8 pages.
48. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2007). (Meta-) Evaluation of Machine Translation. WMT 2007.
49. Philipp Koehn, Nicola Bertoldi, Ondrej Bojar, Chris Callison-Burch, Alexandra Constantin, Brooke Cowan, Chris Dyer, Marcello Federico, Evan Herbst, Hieu Hoang, Christine Moran, Wade Shen, and Richard Zens (2007). Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Confusion Network Decoding. CLSP Summer Workshop Final Report WS, Johns Hopkins University 2007.
50. Alexandra Birch, Chris Callison-Burch and Miles Osborne (2006). Constraining the Phrase-Based, Joint Probability Statistical Translation Model. WMT 2006.
51. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). A Compact Data Structure for Searchable Translation Memories. EAMT 2005.
52. Chris Callison-Burch (2005). Linear B System Description for the 2005 NIST MT Evaluation Exercise. Machine Translation Evaluation Workshop 2005.
53. Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, Chris Callison-Burch, Miles Osborne, and David Talbot (2005). Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation. IWSLT 2005.
54. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Searchable Translation Memories. ASLIB Translating and the Computer 2004.
55. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Improved Statistical Translation Through Editing. EAMT 2004.
56. Chris Callison-Burch and Miles Osborne (2003). Bootstrapping Parallel Corpora. NAACL workshop Building and Using Parallel Texts 2003. 6 pages.
57. Chris Callison-Burch and Miles Osborne (2003). Co-training for Statistical Machine Translation. the 6th Annual CLUK Research Colloquium 2003.
58. Jochen Leidner and Chris Callison-Burch (2003). Evaluating Question Answering Systems Using FAQ Answer Injection. the 6th Annual CLUK Research Colloquium 2003.
59. Chris Callison-Burch (2001). Upping the Ante for "Best of Breed" Machine Translation Providers. ASLIB Translating and the Computer 2001.
60. Raymond Flournoy and Chris Callison-Burch (2001). Secondary Benefits of Feedback and User Interaction in Machine Translation Tools. MT Summit Workshop 2001.

Theses

1. Chris Callison-Burch (2007). Paraphrasing and Translation. PhD Thesis, University of Edinburgh 2007.
2. Chris Callison-Burch (2002). Co-Training for Statistical Machine Translation. Master's thesis, School offormatics, University of Edinburgh 2002.
3. Chris Callison-Burch (2000). A Computer Model of a Grammar for English Questions. Undergraduate thesis, Symbolic Systems Program, Stanford University 2000. 78 pages.

Invited Talks

1. University of Lorraine, Nancy. Crowdsourcing for NLP (with Karën Fort and Christoper Cieri). April 14, 2021
2. Morgan Stanley. Natural Language Understanding with Paraphrases and Word Embeddings. January 19, 2021
3. Two Sigma. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. August 4, 2020
4. University of Pennsylvania. Panelist for CURF's Research & Fellowships Week. November 19, 2019
5. Temple University. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. November 6, 2019
6. University of Pennsylvania. Panelist for MindCORE's summer program. June 14, 2019
7. Undergraduate Program in Cognitive Science (UPenn). Representing Word Meaning with Vectors. June 5, 2019
8. Talk to online MCIT students (UPenn). Natural Language Understanding with Paraphrases and Word Embeddings. April 25, 2019
9. Vanguard Data Science. Natural Language Understanding with Paraphrases and Word Embeddings. October 26, 2018
10. Michigan Institute for Data Science. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. November 29, 2018
11. Google Research (NYC). Learning Translations Without Parallel Texts. August 14, 2018
12. NSF Convergence Workshop on Crowdsourcing. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. May 18, 2018
13. National Academies of Sciences workshop on Challenges in Machine Generation of Analytic Products from Multi-source Data. Crowdsourcing for Natural Language Processing. August 10, 2017
14. NYU. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. February 2, 2017
15. Columbia University. Large-scale Paraphrasing for Natural Language Generation. September 19, 2016
16. Cornell University. Large-scale Paraphrasing for Natural Language Generation. September 9, 2016
17. University of Alabama at Birmingham. Crowdsourcing Translation. April 17, 2015
18. Drexel University. Crowdsourcing Translation. November 8, 2015
19. CMU. Crowdsourcing Translation. April 7, 2015
20. UC Berkeley. Large-scale Paraphrasing for Natural Language Generation. March 12, 2015
21. Stanford. Crowdsourcing Translation. March 11, 2015
22. Facebook. Crowdsourcing Translation. March 10, 2015
23. Coursera. Crowdsourcing Translation. March 9, 2015
24. Google. Large-scale Paraphrasing for Natural Language Generation. March 9, 2015
25. MIT. Large-scale Paraphrasing for Natural Language Generation. January 14, 2015
26. CMU. Large-scale Paraphrasing for Natural Language Generation. November 21, 2014
27. Microsoft Research. Large-scale Paraphrasing for Natural Language Generation. October 3, 2014
28. University of Washington, Seattle. Large-scale Paraphrasing for Natural Language Generation. October 2, 2014
29. The Allen Institute for Artificial Intelligence (AI2). Large-scale Paraphrasing for Natural Language Generation. October 1, 2014
30. Yahoo! Research Labs. Large-scale Paraphrasing for Natural Language Generation. July 29, 2014
31. US Army Research Labs DARPA Computer Science Study Group Applied Research Series: Text and Video Analytics Workshop. Language Understanding with the Help of Images. July 16, 2014
32. International Conference on Natural Language Generation. Large-scale Paraphrasing for Natural Language Generation. June 21, 2014
33. LREC Workshop on Building and Using Comparable Corpora. Crowdsourcing Translation. May 27, 2014
34. University of Maryland. Large-scale Paraphrasing for Natural Language Understanding and Generation. April 23, 2014
35. Institute for Research in Cognitive Science, University of Pennsylvania. Large-scale Paraphrasing for Natural Language Understanding and Generation. March 21, 2014
36. 37th Annual Penn Linguistics Colloquium, University of Pennsylvania. The Wisdom of Crowdsourcing. March 22, 2013
37. Johns Hopkins University. Advances to machine translation and language understanding. February 15, 2013
38. Columbia University IGERT distinguished speaker series. The Promise of Crowdsourcing for NLP and other data sciences. March 29, 2013
39. UT Austin. Large-scale Paraphrasing for Natural Language Understanding and Generation. December 7, 2012
40. IBM TJ Watson Research Center. Large-scale Paraphrasing for Natural Language Understanding and Generation. November 9, 2012
41. NSF-sponsored Workshop on summarizing speaker's attitude and opinion in conversational speech. When annotation with MTurk works. October 20, 2012
42. Linguistics Data Consortium 20th Anniversary Workshop. The Promise of Crowdsourcing. September 6, 2012
43. Human Language Technology Center of Excellence. Machine Translation of Arabic Dialects. April 3, 2012.
44. Human Language Technology Center of Excellence. Machine Translation at the HLTCOE. March 29, 2012
45. University of Pennsylvania. Advances to machine translation and language understanding. February 28, 2012
46. Carnegie Mellon University. Advances to machine translation and language understanding. February 21, 2012
47. Microsoft Research. Crowdsourcing Translation: Professional Quality from Non-Professionals. June 16, 2011
48. Human Language Technology Center of Excellence. Statistical Machine Translation and Crowdsourcing. March 24, 2011.
49. CrowdFlower Meetup, Washington DC. Crowdsourcing Translation with Amazon's Mechanical Turk. July 25, 2010
50. Human Language Technology Center of Excellence. Crowdsourcing Translation with Amazon's Mechanical Turk. November 23, 2010
51. AAAI Panel on Common Sense Knowledge. Automatic versus Manual Construction of Common Sense Knowledge. November 13, 2010
52. UMass Amherst. Crowdsourcing Translation with Amazon's Mechanical Turk. October 18, 2010
53. Brown University. Syntactic Parsing and Machine Translation. September 9, 2010
54. IARPA. Crowdsourcing Translation. August 5, 2010
55. UMD Workshop on Crowdsourcing Translation. Crowdsourcing Translation with Amazon's Mechanical Turk. June 10, 2010
56. BBN. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. March 11, 2010
57. BBN. Improvements to Urdu-English: SCALE Summer Workshop Results. March 11, 2010
58. University of Washington. Syntactic translation models help for low-resource, verb final languages. February 25, 2010
59. Microsoft Research. Syntactic translation models help for low-resource, verb final languages. February 24, 2010
60. University of Pennsylvania. Syntactic translation models help for low-resource, verb final languages. February 8, 2010
61. NIST. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 18, 2009
62. University of Maryland. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 2, 2009.
63. OHSU, Center for Spoken Language Understanding. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. October 7, 2009
64. OHSU, Center for Spoken Language Understanding. Improvements to Urdu-English: SCALE Summer Workshop Results. October 5, 2009
65. University of Pennsylvania. Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. April 13, 2009
66. University of Maryland. Paraphrasing and Translation. November 28, 2007.
67. Johns Hopkins University. Improving Statistical Machine Translation With Paraphrases and Generalization. December 5, 2006
68. Johns Hopkins University. Factored Translation Models. November 28, 2006
69. University of Pennsylvania. Factored Translation Models. August 23, 2006
70. Carnegie Mellon University. Statistical Machine Translation Using Semi-Supervised Learning. April 18, 2005.

Academic Service

• ABET Coordinator for CIS department, 2021-
• DEI Liaison for CIS 2020-
• University Scholars Faculty Council, 2021-
• Hearing Panels for the Faculty Grievance Commission, 2017-2020

• Senior Area Chair for the Semantics: Sentence-level Semantics, Textual Inference and Other Areas track at ACL-IJCNLP 2021
• Area Chair for NAACL-HLT 2021 Special Theme: New Challenges in NLP: Tasks, Methods, Positions
• Sponsorship Director for the Association for Computational Linguistics, 2020-2023
• Test of Time Award Committee, ACL 2020
• Best Paper Committee, ACL 2020
• Advisory Board, Human Language Technology Center of Excellence (HLTCOE), Johns Hopkins University 2016-present
• Director Search Committee, HLTCOE, Johns Hopkins University 2019-2020
• Secretary-Treasurer for SIGDAT 2016-2018
• General Chair for ACL 2017
• Program Co-Chair for EMNLP 2015
• Past Chair of the NAACL Executive Board 2014-2015
• Chair of the NAACL Executive Board 2012-2013
• Board Member for NAACL Executive Board 2010-2011
• Action Editor for Transactions of the Association for Computational Linguistics 2012-2016
• Editorial Board for Computational Linguistics 2010-2012
• Executive Committee, Institute for Research in Cognitive Science (IRCS), University of Pennsylvania 2013-2016
• Board Member for American Machine Translation Association 2009-2010
• North American Representative for ACL Special Interest Group in Machine Translation 2009-2010
• Area Chair for Generation and Summarization for NAACL 2015
• Area Chair for Machine Translation for EMNLP 2014
• Area Chair for Machine Translation for CoLing 2010
• Area Chair for Machine Translation for EACL 2009
• Co-Organizer of the NAACL 2010 Workshop on Creating Language Data With Mechanical Turk
• Co-Organizer of the Workshop on Statistical Machine Translation 2013 (102+ participants)
• Co-Organizer of the Workshop on Statistical Machine Translation 2012 (90 participants)
• Co-Organizer of the Workshop on Statistical Machine Translation 2011 (133 participants)
• Co-Organizer of the Workshop on Statistical Machine Translation 2010
• Co-Organizer of the Workshop on Statistical Machine Translation 2009 (approximately 125+ participants)
• Co-Organizer of the Workshop on Statistical Machine Translation 2008 (148 participants)
• Co-Organizer of the Workshop on Statistical Machine Translation 2007 (117 participants)
• Co-Organizer of the ACL 2009 Textual Entailment Workshop (30 participants)
• Co-Organizer of the Machine Translation Marathon 2010 (approximately 100 participants)
• Co-Organizer of the Machine Translation Marathon 2009 (approximately 80 participants)
• Co-Organizer of the Machine Translation Marathon 2008 (approximately 70 participants)
• Co-Organizer of the Machine Translation Marathon 2007 (approximately 50 participants)
• Co-Chair of ACL 2005 Student Research Workshop
• Steering Committee for ACL 2011 Workshop on Monolingual Text-To-Text Generation
• Chair of Diversity Committee, JHU Computer Science Department
• Graduate Admissions Committee, JHU Computer Science Department
• Chair of Website Re-Design and Video Archiving Committee, CLSP
• Program Committee Member for EMNLP 2016
• Program Committee Member for NAACL 2016
• Program Committee Member for ACL 2014
• Program Committee Member for CoLing 2014
• Reviewer for HCOMP 2020
• Reviewer for CSCW 2014
• Program Committee Member for ACL 2013
• Program Committee Member for NAACL 2013
• Program Committee Member for CoNLL 2013
• Program Committee Member for CHI 2013
• Program Committee Member for NAACL 2012
• Program Committee Member for AMTA 2012
• Program Committee Member for ACL 2012
• Program Committee Member for EACL 2012
• Program Committee Member for EMNLP 2011
• Program Committee Member for ACL 2011
• Program Committee Member for HCOMP 2011
• Program Committee Member for AAAI 2010
• Program Committee Member for ACL 2010
• Program Committee Member for NAACL 2010
• Program Committee Member for EMNLP 2009
• Program Committee Member for NAACL 2009
• Program Committee Member for EAMT 2009
• Program Committee Member for MT Summit 2009
• Program Committee Member for AMTA 2008
• Program Committee Member for CoLing 2008
• Program Committee Member for ACL 2008
• Program Committee Member for EMNLP 2008
• Program Committee Member for ACL 2007
• Program Committee Member for IJCNLP 2007
• Program Committee Member for ACL 2006
• Program Committee Member for ACL 2006 Student Research Workshop
• Program Committee Member for AMTA 2006
• Program Committee Member for IJCAI 2006
• Program Committee Member for EMNLP 2006
• Reviewer for Journal of Computational Linguistics
• Reviewer for Journal of Transactions of the Association for Computational Linguistics
• Reviewer for Journal of Natural Language Engineering
• Reviewer for Journal of Machine Translation
• Reviewer for Journal of Artificial Intelligence Research
• Reviewer for Journal of Linguistic Issues in Language Technology

Awards

• Ford Motor Company Award for Faculty Advising - This award is presented annually by the undergraduate student body in recognition of faculty dedication in helping students realize their educational, career and personal goals. (2022)
• Ford Motor Company Award for Faculty Advising - This award is presented annually by the undergraduate student body in recognition of faculty dedication in helping students realize their educational, career and personal goals. (2021)

Current PhD Students and Postdocs

1. Andrew Zhu, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2027
2. Artemis Panagopoulou, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2026
3. Liam Dugan, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2026
4. Ajay Patel, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2025
5. Alyssa Hwang, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2025
6. Bryan Li, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2025
7. Samar Haider, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2025
8. Yue Yang, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2025
9. Veronica Qing Lyu, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2024
10. Harry Zhang, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2024
11. Aditya Kashyap, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2024
12. Lara Martin, Postdoc, University of Pennsylvania.

PhDs Graduated

1. Daphne Ippolito, University of Pennsylvania (advisors: Chris Callison-Burch and Doug Eck), "Understanding the Limitations of Using Large Language Models for Text Generation", September 2022.
2. Reno Kriz, University of Pennsylvania (advisors: Chris Callison-Burch and Marianna Apidianaki), "Towards a Practically Useful Text Simplification System", June 2021.
3. Anne Cocos, University of Pennsylvania (advisors: Chris Callison-Burch and Marianna Apidianaki), "Paraphrase-based Models of Lexical Semantics", May 2019.
4. Courtney Napoles, Johns Hopkins University (advisors: Chris Callison-Burch and Benjamin Van Durme), "Monolingual Sentence Rewriting as Machine Translation: Generation and Evaluation", June 2018.
5. Juri Ganitkevitch, Johns Hopkins University (advisor: Chris Callison-Burch), "Large-Scale Paraphrase Extraction and Applications", February 2018.
6. Ellie Pavlick, University of Pennsylvania (advisor: Chris Callison-Burch), "Compositional Lexical Semantics in Natural Language Inference", July 2017.
7. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
8. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
9. Omar Zaidan, Johns Hopkins University (advisor: Chris Callison-Burch), "Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks", April 2012.
10. Lane Schwartz, University of Minnesota (advisors: William Schuler and Chris Callison-Burch), "An Incremental Syntactic Language Model for Statistical Phrase-based Translation", February 2012.

Master's Theses Supervised

1. Anna Orosz, University of Pennsylvania (advisors: Chris Callison-Burch and Lara Martin), "Generating Text-based Adventure Games", December 2021.
2. Sri Sanjeevini Devi Ganni, University of Pennsylvania (advisors: Chris Callison-Burch and Lara Martin), "Narratology and Fan Ficton", May 2021.
3. Jacob Beckerman, University of Pennsylvania (advisor: Chris Callison-Burch), "Graph Alogirthms and Visualization of Complex Legal Contracts", December 2020.
4. Liam Dugan, University of Pennsylvania (advisor: Chris Callison-Burch), "Learning Formality from Japanese-English Parallel Corpora", May 2020.
5. Yonah Mann, University of Pennsylvania (advisor: Chris Callison-Burch), "A Data Set for Training QA Systems to Answer Questions about Novels", May 2020.
6. Maria Kustikova, University of Pennsylvania (advisors: Chris Callison-Burch and Kostas Daniilidis), "Clustering Paraphrases by Word Sense Using Textual and Visual Information", May 2019.
7. Devanshu Jain, University of Pennsylvania (advisor: Chris Callison-Burch), "Machine Transliteration", May 2018.
8. Aditya Kashyap, University of Pennsylvania (advisor: Chris Callison-Burch), "Generalizable Identity Classifiers from Self-Reporting Statements on Reddit", May 2018.
9. Brendan Daniel Callahan, University of Pennsylvania (advisor: Chris Callison-Burch), "Image-based Bilingual Lexicon Induction for Low Resrouce Languages", May 2017.
10. Sneha Rajana, University of Pennsylvania (advisor: Chris Callison-Burch), "Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network", May 2017.
11. Mingkun Gao, University of Pennsylvania (advisor: Chris Callison-Burch), "Crowdsourcing Machine Translation", May 2015.

Thesis Committees

1. Zoey Sha Li, UIUC (advisors: Jiawei Han and Heng Ji), "", .
2. Soham Dan, University of Pennsylvania (advisor: Dan Roth), "Systematic Generalization and Compositionality in Grounded Reasoning", July 2022.
3. Carlos Toxtli-Hernandez, Northeastern University (advisor: Saiph Savage), "Artificial Intelligence Tools to Promote Social Good in Gig Markets", June 2022.
4. Daniel Deutsch, University of Pennsylvania (advisor: Dan Roth), "Methods for Text Summarization Evaluation", April 2022.
5. Jonathan Mallinson, University of Edinburgh (advisor: Mirella Lapata), "Universal Paraphrasing via Machine Translation", June 2021.
6. Stephen Mayhew, University of Pennsylvania (advisor: Dan Roth), "Low-resource Named Entity Recognition", August 2019.
7. João Sedoc, University of Pennsylvania (advisor: Lyle Ungar), "Building and Evaluating Conversational Agents", June 2019.
8. Ming Liu, Monash University (advisors: Wray Buntine and Gholamreza Haffari), "Weak Supervision and Active Learning for Deep Neural Models", May 2019.
9. Muthukumar Chandrasekaran, National University of Singapore (advisor: Min-Yen Kan), "A Discourse Centric Framework for Facilitating Instructor INtervention in MOOC Discussion Forums", April 2019.
10. Shyam Upadhyay, University of Pennsylvania (advisor: Dan Roth), "Exploiting Cross-lingual Representations in Natural Language Processing", February 2019.
11. Daniel Khashabi, University of Pennsylvania (advisor: Dan Roth), "Reasoning-Driven Question-Answering for Natural Language Understanding", February 2019.
12. Courtney Napoles, Johns Hopkins University (advisors: Chris Callison-Burch and Benjamin Van Durme), "Monolingual Sentence Rewriting as Machine Translation: Generation and Evaluation", June 2018.
13. Ting-Hao Kenneth Huang, Carnegie Mellon University (advisor: Jeffrey Bigham), "A Crowd-Powered Conversational Assistant That Automates Itself Over Time", June 2018.
14. Juri Ganitkevitch, Johns Hopkins University (advisor: Chris Callison-Burch), "Large-Scale Paraphrase Extraction and Applications", February 2018.
15. Wang Ling, Carnegie Mellon University (advisors: Alan Black, Isabel Trancoso, Chris Dyer, and Luísa Coheur), "Machine Translation 4 Microblogs", October 2015.
16. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
17. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
18. Paramveer S. Dhillon, University of Pennsylvania (advisors: Lyle Ungar and James Gee), "Advances in Spectral Learning with Application to Text Analysis and Brain Imagine", June 2014.
19. Emily Pitler, University of Pennsylvania (advisors: Mitch Marcus and Sampath Kannan), "Models for Improved Tractability and Accuracy in Dependency Parsing", August 2013.
20. Hala Almaghout, Dublin City University (advisors: Andy Way and Jie Jiang), "CCG-Augmented Hierarchical Phrase-Based Statistical Machine Translation", August 2012.
21. Chang Hu, University of Maryland (advisors: Ben Bederson and Philip Resnik), "Monolingual Machine Translation", July 2012.
22. Emily Tucker Prudhommeaux, Center for Spoken Language Understanding, Oregon Health and Science University (advisor: Brian Roark), "Alignment of Narrative Retellings for Automated Neuropsychological Assessment", July 2012.
23. Omar Zaidan, Johns Hopkins University (advisor: Chris Callison-Burch), "Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks", April 2012.
24. Lane Schwartz, University of Minnesota (advisors: William Schuler and Chris Callison-Burch), "An Incremental Syntactic Language Model for Statistical Phrase-based Translation", February 2012.
25. Aaron B. Phillips, Language Technology Institute, Carnegie Mellon University (advisor: Ralf D. Brown), "Modeling Relevance in Statistical Machine Translation: Scoring Alignment, Context, and Annotations of Translation Instances", February 2012.
26. Zhifei Li, Johns Hopkins University (advisor: Sanjeev Khudanpur), "Discriminative Training and Variational Decoding in Machine Translation Via Novel Algorithms for Weighted Hypergraphs", April 2010.
27. Nitin Madnani, University of Maryland (advisor: Bonnie Dorr), "The Circle of Meaning: From Translation to Paraphrasing and Back", 2010.
28. Yuval Marton, University of Maryland (advisor: Philip Resnik), "Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models", October 2009.
29. Elliott Franco Drabek, Johns Hopkins University (advisor: David Yarowsky), "Translingual Fine-grained Morphosyntactic Analysis and its Application to Machine Translation", October 2009.
30. Roy Tromble, Johns Hopkins University (advisor: Jason Eisner), "Search and Learning for the Linear Ordering Problem with an Application to Machine Translation", April 2009.