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Chris Callison-Burch: CV

(Last updated May 25, 2018)

Employment

Associate Professor
University of Pennsylvania, Philadelphia, PA
June 2017-present
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

Computational Linguistics (CIS 530)
University of Pennsylvania
Spring 2018
Data Structures and Algorithms (CIS 121)
University of Pennsylvania
Fall 2017
This course introduces undergraduates to the fundamental algorithms and data structures that are used in the field of computer science. We introduce methods for analyzing the complexity of an algorithm and predicting the running time of software. We describe data structures like stacks, queues, lists, trees, priority queues, maps, hash tables and graphs, and we discuss how to implement them efficiently and how to use them effectively in software. Course assignments include of weekly assignments consisting of problem sets and programming exercises in Java, and a larger term project.
Data Structures and Algorithms (CIS 121)
University of Pennsylvania
Fall 2016
This course introduces undergraduates to the fundamental algorithms and data structures that are used in the field of computer science. We introduce methods for analyzing the complexity of an algorithm and predicting the running time of software. We describe data structures like stacks, queues, lists, trees, priority queues, maps, hash tables and graphs, and we discuss how to implement them efficiently and how to use them effectively in software. Course assignments include of weekly assignments consisting of problem sets and programming exercises in Java, and a larger term project.
Crowdsourcing and Human Computation (NETS 213)
University of Pennsylvania
Spring 2016
Crowdsourcing and human computation are emerging fields that sit squarely at the intersection of economics and computer science. They examine how people can be used to solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms. Online marketplaces like Mechanical Turk provide an infrastructure that allows micropayments to be given to people in return for completing human intelligence tasks. This opens up previously unthinkable possibilities like people being used as function calls in software. We will investigate how crowdsourcing can be used for computer science applications like machine learning, next-generation interfaces, and data mining. Beyond these computer science aspects, we will also delve into topics like prediction markets, how businesses can capitalize on collective intelligence, and the fundamental principles that underly democracy and other group decision-making processes.
Data Structures and Algorithms (CIS 121)
University of Pennsylvania
Fall 2015
Machine Translation (CIS 526)
University of Pennsylvania
Spring 2015
Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems like Google Translate and Bing Translator learn how to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence.
Crowdsourcing and Human Computation (NETS 213)
University of Pennsylvania
Fall 2014
Machine Translation (CIS 526)
University of Pennsylvania
Spring 2014
Crowdsourcing and Human Computation (CIS 399)
University of Pennsylvania
Fall 2013
Machine Translation (600.466) co-taught with Adam Lopez and Matt Post
Johns Hopkins University
Spring 2012
Seminar on the Meaning, Translation and Generation of Text (600.466) co-taught with Ben Van Durme
Johns Hopkins University
Fall and Spring 2011 and 2012
Seminar in Machine Translation (600.466)
Johns Hopkins University
Fall and Spring 2007-2010

Teaching Reviews at Penn

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
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
CI-NEW: NIEUW: Novel Incentives and Workflows in Linguistics Data Collection NSF $1,218,465 July 14, 2017-June 30, 2020 co-PI with Christopher Cieri and Mark Liberman
Learning translations from monolingual texts (LORELEI) DARPA $478,000 2015-2019 PI at Penn

Pending grants

Grant Title Awarding Body Amount Dates PI Info
Semi-supervised Learning of Multimodal Representations DARPA (in preparation) $200k 2019-2020 Chris Callison-Burch and Derry Wijaya
FW-HTF Theme 1: Enabling Rural Digital Workers to Collaborate with AI to Learn Skills Over Time, Increase Wages and Opportunities, and Access Creative Work NSF (in preparation) $3m 2019-2024 Jeffrey Bigham (CMU) - PI, Chris Callison-Burch (UPenn), Ben Hanrahan (Penn State), Aniket Kittur (CMU), Saiph Savage (West Virgina University)
STTR: Personalized Retrieval-based Simplification NSF (in preparation) $225k 2019-2023 Eleni Miltsakaki (Choosito.com) - PI, Chris Callison-Burch
RIME: Real-tIme Multimodal Evidence-based Reasoning ONR (submitted) $2m 2019-2023 Zack Ives - PI, Dan Roth, Chris Callison-Burch, Susan Davidson, Mark Sammons
Learning to Reason with Learned Models and Domain Knowledge ONR (submitted) $2m 2019-2023 Dan Roth - PI, Zack Ives, Chris Callison-Burch, Susan Davidson, Mark Sammons
Using Artificial Intelligence to Understand Gun Violence in America University of Pennsylvania URF (submitted) $50k 2019-2020 Chris Callison-Burch

Past grants

Grant Title Awarding Body Amount Dates PI Info
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
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. 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.
  2. Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki and Chris Callison-Burch (2018). Simplification Using Paraphrases and Context-based Lexical Substitution. NAACL 2018. 12 pages.
  3. 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.
  4. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2018). Comparing Constraints for Taxonomic Organization. NAACL 2018. 12 pages.
  5. 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. Honourable Mention Award. 12 pages.
  6. 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.
  7. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2017). Mapping the Paraphrase Database to WordNet. STARSEM 2017.
  8. Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki and Vered Shwartz (2017). Learning Antonyms with Paraphrases and a Morphology-aware Neural Network. STARSEM 2017.
  9. Ann Cocos and Chris Callison-Burch (2017). The Language of Place: Semantic Value from Geospatial Context. EACL 2017. Short papers. 5 pages.
  10. 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.
  11. Ellie Pavlick and Chris Callison-Burch (2016). Tense Manages to Predict Implicative Behavior in Verbs. EMNLP 2016. Short papers. 5 pages.
  12. Ellie Pavlick and Chris Callison-Burch (2016). So-Called Non-Subsective Adjectives. STARSEM 2016. Best Paper Award. 6 pages.
  13. 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.
  14. Ellie Pavlick and Chris Callison-Burch (2016). Simple PPDB: A Paraphrase Database for Simplification. ACL 2016. Short papers. 6 pages.
  15. Anne Cocos and Chris Callison-Burch (2016). Clustering Paraphrases by Word Sense. NAACL 2016. 10 pages.
  16. Courtney Napoles, Chris Callison-Burch, and Matt Post (2016). Sentential Paraphrasing as Black-Box Machine Translation. NAACL 2016. Short papers. 5 pages.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. Mingkun Gao, Wei Xu, and Chris Callison-Burch (2015). Cost Optimization for Crowdsourcing Translation. NAACL 2015. 9 pages.
  22. Heba Elfardy, Mona Diab and Chris Callison-Burch (2015). Ideological Perspective Detection Using Semantic Features. STARTSEM 2015. 10 pages.
  23. Ann Irvine and Chris Callison-Burch (2014). Hallucinating Phrase Translations for Low Resource MT. CoNLL 2014. 11 pages.
  24. 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.
  25. Jonathan Weese, Juri Ganitkevitch, and Chris Callison-Burch (2014). PARADIGM: Paraphrase Diagnostics through Grammar Matching. EACL 2014. 10 pages.
  26. Ellie Pavlick, Rui Yan, and Chris Callison-Burch (2014). Crowdsourcing for Grammatical Error Correction. CSCW Poster 2014. 4 pages.
  27. Juri Ganitkevitch and Chris Callison-Burch (2014). The Multilingual Paraphrase Database. LREC 2014. 8 pages.
  28. Ann Irvine, Joshua Langfus, and Chris Callison-Burch (2014). The American Local News Corpus. LREC 2014. 4 pages.
  29. Ryan Cotterell and Chris Callison-Burch (2014). A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic. LREC 2014. 5 pages.
  30. Xuchen Yao, Ben Van Durme, Chris Callison-Burch and Peter Clark (2013). Semi-Markov Phrase-based Monolingual Alignment. EMNLP 2013. 11 pages.
  31. 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.
  32. 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.
  33. 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.
  34. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2013). PPDB: The Paraphrase Database. NAACL 2013. Short papers. 7 pages.
  35. Ann Irvine and Chris Callison-Burch (2013). Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals. NAACL 2013. Short papers. 6 pages.
  36. 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.
  37. Xuchen Yao, Benjamin Van Durme and Chris Callison-Burch (2012). Expectations of Word Sense in Parallel Corpora. NAACL 2012. Short papers. 5 pages.
  38. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2012). Monolingual Distributional Similarity for Text-to-Text Generation. STARSEM 2012. 9 pages.
  39. 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.
  40. Alex Klementiev, Ann Irvine, Chris Callison-Burch, and David Yarowsky (2012). Toward Statistical Machine Translation without Parallel Corpora. EACL 2012. 11 pages.
  41. 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.
  42. 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.
  43. Omar Zaidan and Chris Callison-Burch (2011). Crowdsourcing Translation: Professional Quality from Non-Professionals. ACL 2011. 10 pages.
  44. Lane Schwartz, Chris Callison-Burch, William Schuler and Stephen Wu (2011). Incremental Syntactic Language Models for Phrase-based Translation. ACL 2011. 12 pages.
  45. Omar Zaidan and Chris Callison-Burch (2010). Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators. NAACL 2010. Short papers. 4 pages.
  46. 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.
  47. Ann Irvine, Alex Klementiev, and Chris Callison-Burch (2010). Transliterating From All Languages. AMTA 2010. 8 pages.
  48. Michael Bloodgood and Chris Callison-Burch (2010). Large-Scale, Cost-Focused Active Learning for Statistical Machine Translation. ACL 2010. 11 pages.
  49. Abby Levenberg, Chris Callison-Burch, and Miles Osborne (2010). Stream-based Translation Models for Statistical Machine Translation. NAACL 2010. 9 pages.
  50. Scott Novotney and Chris Callison-Burch (2010). Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription. NAACL 2010. 9 pages.
  51. Chris Callison-Burch (2009). Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk. EMNLP 2009. 10 pages.
  52. Omar Zaidan and Chris Callison-Burch (2009). Feasibility of Human-in-the-loop Minimum Error Rate Training. EMNLP 2009. 10 pages.
  53. Yuval Marton, Chris Callison-Burch and Philip Resnik (2009). Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases. EMNLP 2009. 10 pages.
  54. 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.
  55. Chris Callison-Burch (2008). Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. EMNLP 2008. 10 pages.
  56. Chris Callison-Burch, Trevor Cohn, Mirella Lapata (2008). ParaMetric: An Automatic Evaluation Metric for Paraphrasing. CoLing 2008. 8 pages.
  57. 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.
  58. Chris Callison-Burch, Philipp Koehn and Miles Osborne (2006). Improved Statistical Machine Translation Using Paraphrases. NAACL 2006.
  59. Chris Callison-Burch, Miles Osborne and Philipp Koehn (2006). Re-evaluating the Role of Bleu in Machine Translation Research. EACL 2006. 8 pages.
  60. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). Scaling Phrase-Based Statistical Machine Translation to Larger Corpora and Longer Phrases. ACL 2005.
  61. Colin Bannard and Chris Callison-Burch (2005). Paraphrasing with Bilingual Parallel Corpora. ACL 2005.
  62. Chris Callison-Burch, David Talbot and Miles Osborne (2004). Statistical Machine Translation with Word- and Sentence-Aligned Parallel Corpora. ACL 2004.
  63. 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. 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.
  2. Wei Xu, Courtney Napoles, Ellie Pavlick, Jim Chen, and Chris Callison-Burch (2016). Optimizing Statistical Machine Translation for Text Simplification. TACL 2016. 15 pages.
  3. Ann Irvine and Chris Callison-Burch (2016). A Comprehensive Analysis of Bilingual Lexicon Induction. Computational Linguistics 2016. 38 pages.
  4. 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.
  5. Wei Xu, Chris Callison-Burch, and Courtney Napoles (2015). Problems in Current Text Simplification Research: New Data Can Help. TACL 2015. 16 pages.
  6. Omar Zaidan and Chris Callison-Burch (2014). Arabic Dialect Identification. Computational Linguistics 2014. 36 pages.
  7. Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan and Yangfeng Ji (2014). Extracting Lexically Divergent Paraphrases from Twitter. TACL 2014. 14 pages.
  8. Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, and Chris Callison-Burch (2014). The Language Demographics of Amazon Mechanical Turk. TACL 2014. 13 pages.
  9. 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.
  10. 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.
  11. 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.
  12. Jonathan Weese and Chris Callison-Burch (2010). Visualizing Data Structures in Parsing-Based Machine Translation. PBML 2010. 10 pages.
  13. Lane Schwartz and Chris Callison-Burch (2010). Hierarchical Phrase-Based Grammar Extraction in Joshua: Suffix Arrays and Prefix Trees. PBML 2010. 10 pages.
  14. Zhifei Li, Chris Callison-Burch, Sanjeev Khudanpur, and Wren Thornton (2009). Decoding in Joshua: Open Source, Parsing-Based Machine Translation. PBML 2009. 10 pages.
  15. 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. 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.
  2. 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.
  3. 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.
  4. Ellie Pavlick and Chris Callison-Burch (2016). The Gun Violence Database. Bloomberg Data for Good Exchange 2016. 6 pages.
  5. Wei Xu, Chris Callison-Burch, and Bill Dolan (2015). SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter. SemEval 2015. 11 pages.
  6. 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.
  7. 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.
  8. Ellie Pavlick and Chris Callison-Burch (2015). Extracting Structured Information via Automatic + Human Computation. HCOMP 2015. 2 pages.
  9. 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.
  10. 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.
  11. Chris Callison-Burch (2014). Crowd-Workers: Aggregating Information Across Turkers To Help Them Find Higher Paying Work. HCOMP Poster 2014. 2 pages.
  12. Ann Irvine and Chris Callison-Burch (2014). Using Comparable Corpora to Adapt MT Models to New Domains. WMT 2014. 8 pages.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Ann Irvine and Chris Callison-Burch (2013). Combining Bilingual and Comparable Corpora for Low Resource Machine Translation. WMT 2013. 9 pages.
  18. 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.
  19. Matt Post, Chris Callison-Burch, and Miles Osborne (2012). Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing. WMT 2012. 9 pages.
  20. Jonathan Weese, Chris Callison-Burch, and Adam Lopez (2012). Using Categorial Grammar to Label Translation Rules. WMT 2012. 10 pages.
  21. Juri Ganitkevitch, Yuan Cao, Jonathan Weese, Matt Post, and Chris Callison-Burch (2012). Joshua 4.0: Packing, PRO, and Paraphrases. WMT 2012. 9 pages.
  22. 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.
  23. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan (2011). Findings of the 2011 Workshop on Statistical Machine Translation. WMT 2011. 43 pages.
  24. Charley Chan, Chris Callison-Burch, and Benjamin Van Durme (2011). Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity. GEMS 2011. 10 pages.
  25. 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.
  26. 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.
  27. Courtney Napoles, Ben Van Durme (2011). Evaluating sentence compression: Pitfalls and suggested remedies. Workshop on Monolingual Text-To-Text Generation 2011. 7 pages.
  28. 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.
  29. Rui Wang and Chris Callison-Burch (2011). Paraphrase Fragment Extraction from Monolingual Comparable Corpora. BUCC 2011. 9 pages.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. Chris Callison-Burch, Philipp Koehn, Christof Monz and Josh Schroeder (2009). Findings of the 2009 Workshop on Statistical Machine Translation. WMT 2009. 28 pages.
  37. 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.
  38. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2008). Further Meta-Evaluation of Machine Translation. WMT 2008. 37 pages.
  39. 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.
  40. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2007). (Meta-) Evaluation of Machine Translation. WMT 2007.
  41. 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.
  42. Alexandra Birch, Chris Callison-Burch and Miles Osborne (2006). Constraining the Phrase-Based, Joint Probability Statistical Translation Model. WMT 2006.
  43. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). A Compact Data Structure for Searchable Translation Memories. EAMT 2005.
  44. Chris Callison-Burch (2005). Linear B System Description for the 2005 NIST MT Evaluation Exercise. Machine Translation Evaluation Workshop 2005.
  45. 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.
  46. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Searchable Translation Memories. ASLIB Translating and the Computer 2004.
  47. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Improved Statistical Translation Through Editing. EAMT 2004.
  48. Chris Callison-Burch and Miles Osborne (2003). Bootstrapping Parallel Corpora. NAACL workshop Building and Using Parallel Texts 2003. 6 pages.
  49. Chris Callison-Burch and Miles Osborne (2003). Co-training for Statistical Machine Translation. the 6th Annual CLUK Research Colloquium 2003.
  50. Jochen Leidner and Chris Callison-Burch (2003). Evaluating Question Answering Systems Using FAQ Answer Injection. the 6th Annual CLUK Research Colloquium 2003.
  51. Chris Callison-Burch (2001). Upping the Ante for "Best of Breed" Machine Translation Providers. ASLIB Translating and the Computer 2001.
  52. 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. NYU. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. February 2, 2017
  2. Columbia University. Large-scale Paraphrasing for Natural Language Generation. September 19, 2016
  3. Cornell University. Large-scale Paraphrasing for Natural Language Generation. September 9, 2016
  4. University of Alabama at Birmingham. Crowdsourcing Translation. April 17, 2015
  5. Drexel University. Crowdsourcing Translation. November 8, 2015
  6. CMU. Crowdsourcing Translation. April 7, 2015
  7. UC Berkeley. Large-scale Paraphrasing for Natural Language Generation. March 12, 2015
  8. Stanford. Crowdsourcing Translation. March 11, 2015
  9. Facebook. Crowdsourcing Translation. March 10, 2015
  10. Coursera. Crowdsourcing Translation. March 9, 2015
  11. Google. Large-scale Paraphrasing for Natural Language Generation. March 9, 2015
  12. MIT. Large-scale Paraphrasing for Natural Language Generation. January 14, 2015
  13. CMU. Large-scale Paraphrasing for Natural Language Generation. November 21, 2014
  14. Microsoft Research. Large-scale Paraphrasing for Natural Language Generation. October 3, 2014
  15. University of Washington, Seattle. Large-scale Paraphrasing for Natural Language Generation. October 2, 2014
  16. The Allen Institute for Artificial Intelligence (AI2). Large-scale Paraphrasing for Natural Language Generation. October 1, 2014
  17. Yahoo! Research Labs. Large-scale Paraphrasing for Natural Language Generation. July 29, 2014
  18. 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
  19. International Conference on Natural Language Generation. Large-scale Paraphrasing for Natural Language Generation. June 21, 2014
  20. LREC Workshop on Building and Using Comparable Corpora. Crowdsourcing Translation. May 27, 2014
  21. University of Maryland. Large-scale Paraphrasing for Natural Language Understanding and Generation. April 23, 2014
  22. Institute for Research in Cognitive Science, University of Pennsylvania. Large-scale Paraphrasing for Natural Language Understanding and Generation. March 21, 2014
  23. 37th Annual Penn Linguistics Colloquium, University of Pennsylvania. The Wisdom of Crowdsourcing. March 22, 2013
  24. Johns Hopkins University. Advances to machine translation and language understanding. February 15, 2013
  25. Columbia University IGERT distinguished speaker series. The Promise of Crowdsourcing for NLP and other data sciences. March 29, 2013
  26. UT Austin. Large-scale Paraphrasing for Natural Language Understanding and Generation. December 7, 2012
  27. IBM TJ Watson Research Center. Large-scale Paraphrasing for Natural Language Understanding and Generation. November 9, 2012
  28. NSF-sponsored Workshop on summarizing speaker's attitude and opinion in conversational speech. When annotation with MTurk works. October 20, 2012
  29. Linguistics Data Consortium 20th Anniversary Workshop. The Promise of Crowdsourcing. September 6, 2012
  30. Human Language Technology Center of Excellence. Machine Translation of Arabic Dialects. April 3, 2012.
  31. Human Language Technology Center of Excellence. Machine Translation at the HLTCOE. March 29, 2012
  32. University of Pennsylvania. Advances to machine translation and language understanding. February 28, 2012
  33. Carnegie Mellon University. Advances to machine translation and language understanding. February 21, 2012
  34. Microsoft Research. Crowdsourcing Translation: Professional Quality from Non-Professionals. June 16, 2011
  35. Human Language Technology Center of Excellence. Statistical Machine Translation and Crowdsourcing. March 24, 2011.
  36. CrowdFlower Meetup, Washington DC. Crowdsourcing Translation with Amazon's Mechanical Turk. July 25, 2010
  37. Human Language Technology Center of Excellence. Crowdsourcing Translation with Amazon's Mechanical Turk. November 23, 2010
  38. AAAI Panel on Common Sense Knowledge. Automatic versus Manual Construction of Common Sense Knowledge. November 13, 2010
  39. UMass Amherst. Crowdsourcing Translation with Amazon's Mechanical Turk. October 18, 2010
  40. Brown University. Syntactic Parsing and Machine Translation. September 9, 2010
  41. IARPA. Crowdsourcing Translation. August 5, 2010
  42. UMD Workshop on Crowdsourcing Translation. Crowdsourcing Translation with Amazon's Mechanical Turk. June 10, 2010
  43. BBN. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. March 11, 2010
  44. BBN. Improvements to Urdu-English: SCALE Summer Workshop Results. March 11, 2010
  45. University of Washington. Syntactic translation models help for low-resource, verb final languages. February 25, 2010
  46. Microsoft Research. Syntactic translation models help for low-resource, verb final languages. February 24, 2010
  47. University of Pennsylvania. Syntactic translation models help for low-resource, verb final languages. February 8, 2010
  48. NIST. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 18, 2009
  49. University of Maryland. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 2, 2009.
  50. OHSU, Center for Spoken Language Understanding. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. October 7, 2009
  51. OHSU, Center for Spoken Language Understanding. Improvements to Urdu-English: SCALE Summer Workshop Results. October 5, 2009
  52. University of Pennsylvania. Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. April 13, 2009
  53. University of Maryland. Paraphrasing and Translation. November 28, 2007.
  54. Johns Hopkins University. Improving Statistical Machine Translation With Paraphrases and Generalization. December 5, 2006
  55. Johns Hopkins University. Factored Translation Models. November 28, 2006
  56. University of Pennsylvania. Factored Translation Models. August 23, 2006
  57. Carnegie Mellon University. Statistical Machine Translation Using Semi-Supervised Learning. April 18, 2005.

Academic Service

Current PhD Students and Postdocs

  1. Marianna Apidianaki, Visiting Scholar, Centre national de la recherche scientifique.
  2. Anietie Andy, Postdoc, University of Pennsylvania (PhD from Howard University).
  3. Derry Wijaya, Postdoc, University of Pennsylvania (PhD from CMU).
  4. Daniel Deutsch, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2022
  5. Danni Ma, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2022
  6. Hangfeng He, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2022
  7. Jie Gao, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2021
  8. Reno Kriz, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2021
  9. Daphne Ippolito, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2020
  10. Anne Cocos, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2019
  11. Jonny Weese, PhD Student, Johns Hopkins University. Expected graduation date: Fall 2017

PhDs Graduated

  1. Courtney Napoles, Johns Hopkins University (advisor: ), "Monolingual Sentence Rewriting as Machine Translation: Generation and Evaluation", June 2018.
  2. Juri Ganitkevitch, Johns Hopkins University (advisor: Chris Callison-Burch), "Large-Scale Paraphrase Extraction and Applications", February 2018.
  3. Ellie Pavlick, University of Pennsylvania (advisor: Chris Callison-Burch), "Compositional Lexical Semantics in Natural Language Inference", July 2017.
  4. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
  5. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
  6. Omar Zaidan, Johns Hopkins University (advisor: Chris Callison-Burch), "Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks", April 2012.
  7. Lane Schwartz, University of Minnesota (advisors: William Schuler and Chris Callison-Burch), "An Incremental Syntactic Language Model for Statistical Phrase-based Translation", February 2012.

Thesis Committees

  1. Wang Ling, Carnegie Mellon University (advisors: Alan Black, Isabel Trancoso, Chris Dyer, and Luísa Coheur), "Machine Translation 4 Microblogs", October 2015.
  2. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
  3. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
  4. 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.
  5. Emily Pitler, University of Pennsylvania (advisors: Mitch Marcus and Sampath Kannan), "Models for Improved Tractability and Accuracy in Dependency Parsing", August 2013.
  6. Hala Almaghout, Dublin City University (advisors: Andy Way and Jie Jiang), "CCG-Augmented Hierarchical Phrase-Based Statistical Machine Translation", August 2012.
  7. Chang Hu, University of Maryland (advisors: Ben Bederson and Philip Resnik), "Monolingual Machine Translation", July 2012.
  8. 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.
  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.
  11. 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.
  12. Zhifei Li, Johns Hopkins University (advisor: Sanjeev Khudanpur), "Discriminative Training and Variational Decoding in Machine Translation Via Novel Algorithms for Weighted Hypergraphs", April 2010.
  13. Nitin Madnani, University of Maryland (advisor: Bonnie Dorr), "The Circle of Meaning: From Translation to Paraphrasing and Back", 2010.
  14. Yuval Marton, University of Maryland (advisor: Philip Resnik), "Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models", October 2009.
  15. Elliott Franco Drabek, Johns Hopkins University (advisor: David Yarowsky), "Translingual Fine-grained Morphosyntactic Analysis and its Application to Machine Translation", October 2009.
  16. Roy Tromble, Johns Hopkins University (advisor: Jason Eisner), "Search and Learning for the Linear Ordering Problem with an Application to Machine Translation", April 2009.

Undergraduate and Masters Advising

Independent Studies and RAships

Summer 2018

  1. Minh Nguyen - Undergraduate (Swartmore) - RA (Swartmore Undergraduate Research Award)
  2. Jai Thirani - Undergraduate - RA
  3. Arun Kirubarajan - Undergraduate - RA (PURM)
  4. Daniel Stekol - Undergraduate - RA (PURM)
  5. Veronica (Qing) Lyu - Undergraduate (Tsinghua University) - Independent Study

Spring 2018

  1. Zhan Chin - Undergraduate - Independent Study
  2. Luke Carlson - Master's (Submatricated) - Independent Study
  3. Shrinidhi Ramakrishnan - Master's - Independent Study
  4. Maria Kustikova - Master's - Independent Study
  5. Gil Landau - Master's - Independent Study
  6. Jasmine (Sun Jae) Lee - Undergraduate - RA

Fall 2017

  1. Alexander Ma - Undergraduate - Independent Study
  2. Priya Gupta - Undergraduate - RA
  3. Devanshu Jain - Master's - Master's Thesis
  4. Harshal Godhia - Master's - Independent Study
  5. Zhiyu Ma - Master's - Independent Study
  6. Aliza Hochsztein - Undergraduate - Senior Thesis

Summer 2017

  1. Eddie Okon - Undergraduate - RA
  2. Natasha Ter-Saakov - High school / Undergraduate - RA
  3. Maryanne Cosgrove - Undergraduate - RA
  4. Bradley Jackson - Undergraduate - RA

Spring 2017

  1. Grace Arnold - Undergraduate - RA
  2. Jack Liu - Undergraduate - Senior Thesis
  3. Jessica Li - Undergraduate - RA
  4. Jianging Wang - Undergraduate - RA
  5. John Hewitt - Undergraduate - RA
  6. Sara Dwyer - Undergraduate - RA
  7. Veronica Wharton - Undergraduate - Senior Project
  8. Victoria Xiao - Undergraduate - RA
  9. Brendan Callahan - Master's - Independent Study
  10. Devanshu Jain - Master's - Independent Study
  11. Sneha Rajana - Master's - Master's Thesis
  12. Xiao Ling - Master's - Master's Thesis

Fall 2016

  1. John Hewitt - Undergraduate - RA
  2. Nivedita Sankar - Undergraduate - Senior Design
  3. Sierra Yit - Undergraduate - Independent Study
  4. Talia Delijani - Undergraduate - RA
  5. Veronica Wharton - Undergraduate - Senior Project
  6. Victoria Xiao - Undergraduate - RA
  7. Brendan Callahan - Master's - Independent Study
  8. Sneha Rajana - Master's - RA
  9. Xiao Ling - Master's - Independent Study

Summer 2016

  1. John Hewitt - Undergraduate - RA
  2. Parker Stakoff - Undergraduate - RA
  3. Veronica Wharton - Undergraduate - RA
  4. Victoria Xiao - Undergraduate - RA

Spring 2016

  1. Lawrence Chan - Undergraduate - RA
  2. Alexander Ma - Undergraduate - Independent Study
  3. Parker Stakoff - Undergraduate - RA
  4. Veronica Wharton - Undergraduate - RA, Independent Study
  5. Sierra Yit - Undergraduate - RA, TA
  6. Theresa Breiner - Master's - Independent Study
  7. Brendan Callahan - Master's - Independent Study
  8. Shreejit Gangadharan - Master's - RA
  9. Grishma Jena - Master's - Independent Study
  10. Sneha Rajana - Master's - Independent Study
  11. Dhruvil Shah - Master's - RA
  12. Ishan Srivastava - Master's - RA

Fall 2015

  1. Drew Stone - Undergraduate - RA
  2. Anwesha Das - Master's - Independent Study
  3. Deepti Panuganti - Master's - Independent Study
  4. Siyu Qiu - Master's - Independent Study
  5. Shreejit Gangadharan - Master's - RA
  6. Dhruvil Shah - Master's - RA
  7. Sierra Yit - Undergraduate - RA, TA
  8. Veronica Wharton - Undergraduate - Independent Study

Team Projects

2017-2018

  1. Mindful app
    • Anvita Achar
    • Daniel Moreno
    • Francisco Selame
    • Karinna Loo
  2. Machine Learning for Gun Violence Advocacy
    • Ben Sandloer
    • Elizabeth Hamp
    • Yoni Nachmany
    • Allison Schwartz
    • Anosha Minai
  3. Aggregating Insights from Amazon Product Reviews
    • Carolina Zheng
    • Joe Cappadona
    • Raymond Yin
    • Sumit Shyamsukha
  4. Reddermatology
    • Eddie Okon
    • Vishnu Rachakonda
    • Adam Yunus
    • Vamsee
  5. Get2KnowUs
    • Natasha Narang
    • Sammi Caby
    • Vivian Ge
    • Matt Cohen
  6. Translating Text with Images - Honorable Mention in the CIS Senior Design fair
    • John Hewitt
  7. Know Your Nyms
    • Carrie (Yuqing) Wang

Fall 2016

  1. NETS 213 Prize: Crowdsourced Interpretation of Terms of Service
    • Elizabeth Hamp
    • Grace Arnold
    • Mara Levy
    • Yoni Nachmany

2016-2017

  1. Senior Design Project: Know Your Nyms
    • Dean Fulgoni
    • Hannah Cutler
    • Ross Mechanic
  2. Senior Thesis: Discovering Meronyms using Deep Learning
    • Jack Liu
  3. Senior Design Project: Data-Driven Understanding of Adjectives in Yelp Reviews - Honorable Mention in the CIS Senior Design fair
    • Veronica Wharton
  4. Senior Design Project: Trending Topics on Wikipedia - ESE Societal Impact Award
    • Abhiti Prabahar
    • Alice Serfati
    • Cristina Bustamant
    • Sierra Yit

2015-2016

  1. Senior Design Project: Visualizing Gun Violence
    • Fabian Wikstroem
    • Alexandra Selldorff
    • Michael Browne
    • Nina Ilieva
  2. Senior Design Project: Mobile Crowdsourcing - Honorable Mention in the CIS Senior Design fair
    • Kate Miller
    • Alex Whitaker
    • Zach Krasner
    • Laura Kingsley
  3. Senior Design Project: Machine Learning for MeetForCoffee
    • Hong Kim
    • Minsu Kim
  4. Senior Thesis: Natural Language Understanding with Paraphrases
    • Yuqi Zhu

2014-2015

  1. Senior Design Project: Observing Republican and Democrat Behavior through Tweeted Articles
    • Dhruv Maheshwari
    • Ali Altaf
    • Hamza Qaiser
    • Dennis Sell
  2. Senior Design Project: University Depression Monitoring Using Twitter Data
    • Ashwin Baweja
    • Jason Kong
    • Tommy Pan Fang
    • Yaou Wang
  3. Senior Design Project: Generating Music with Machine Translation Algorithms - Honorable Mention in the CIS Senior Design fair
    • Rigel Swavely
    • Nicole Limtiaco

2013-2014

  1. Senior Design Project: Bachify - Machine Learning for Bach chorales - 1st prize in the CIS Senior Design fair
    • Israel Geselowitz
    • David Cerny
    • Jiten Suthar