Wei Xu     

[phonetic pronunciation: way shoo ]

Postdoctoral Fellow
Computer and Information Science Department
University of Pennsylvania
   Levine Hall Room 361
3330 Walnut St, Philadelphia, PA 19104

I am a postdoc at University of Pennsylvania, working with Chris Callison-Burch. My research lies at the intersections of machine learning, natural language processing, and social media. I am particularly interested in designing learning algorithms for gleaning semantic and structured knowledge from massive social media and web data. My work enables deeper analysis of text meaning and better natural language generation. I am a workshop co-chair for ACL 2017, an area chair for EMNLP 2016 and the publicity chair for NAACL 2016.

I will be starting as an assistant professor in the Department of Computer Science and Engineering at the Ohio State University in fall 2016. I will be looking for multiple PhD students.

I wrote an ultimate Twitter API tutorial.
What's New
  Aug 6-14, Berlin for ACL, presenting my 3rd TACL paper on text simplification
  Dec 10-16, Osaka Japan for COLING, organizing the 2nd Workshop on Noisy User-generated Text
Talk on Text Simplification @ EMNLP 2015
Talk on Twitter Paraphrase @ NAACL 2015

I designed and taught a new course — Social Media and Text Analytics.

[Summary] Social media provides a massive amount of valuable information and shows us how language is actually used by lots of people. This course covers several important machine learning algorithms and the core natural language processing techniques for obtaining and processing Twitter data.


My past advisees all have published one or more papers with me:
    Quanze Chen (undergraduate UPenn → PhD University of Washington)
    Bin Fu (undergraduate Tsinghua → PhD CMU → Google NYC)
    Mingkun Gao (master Upenn → PhD UIUC)
    Ray Lei (undergraduate UPenn → master UPenn)
    Maria Pershina (PhD NYU | I served on her PhD thesis committee)
    Siyu Qiu (master UPenn → Hulu)

Research Highlights

Joint Word-Sentence Models

I build probabilistic graphical models to extract semantic or structured knowledge from large volumes of data. I designed the first succesful models to extract paraphrases from Twitter that can scale up to billions of sentences. These web-scale paraphrases enable natural language systems to handle errors (e.g. “everytime” ↔ “every time”), lexical variations (e.g. “oscar nom’d doc” ↔ “Oscar-nominated documentary”), rare words (e.g “NetsBulls series” ↔ “Nets and Bulls games”), and language shifts (e.g. “is bananas” ↔ “is great”) [BUCC2013] [SemEval2015]. But it is difficult to capture such lexically divergent paraphrases by the conventional similarity-based approaches. I invented the multi-instance learning paraphrase (MultiP) model [TACL2014], which jointly infers latent word-sentence relations and relaxes the reliance on human annotation. It is a conditional random field model with latent variables [ACL2014][ACL2013], and the current state-of-the-art, outperforming deep leaning and latent space methods.

Statistical Natural Language Generation (NLG) Framework

Many text-to-text generation problems can be thought of as sentential paraphrasing or monolingual machine translation. It faces an exponential search space larger than bilingual translation, but a much smaller optimal solution space due to specific task requirements. I advocate for a statistical text-to-text framework, building on top of statistical machine translation (SMT) technology. My recent work uncovered multiple serious problems in text simplification [TACL2015] research between 2010 and 2014, and set a new state-of-the-art by designing novel objective functions for optimizing syntax-based SMT and overgenerating with large-scale paraphrases [TACL2016]. I am also very interested in paraphrases of different language styles (e.g. historic ↔ modern [COLING2012], erroneous ↔ well-edited [BUCC2013], feminine ↔ masculine [AAAI2016]).

Professional Service
Area Chair:   EMNLP (2016)
Publicity Chair:   NAACL (2016)
Session Chair:   EMNLP (2015), NAACL (2015), AAAI (2015), ACL (2014)
     - ACL 2015 Workshop on Noisy User-generated Text (W-NUT)
     - SemEval 2015 shared-task: Paraphrases and Semantic Similarity in Twitter (PIT)
     - 2016 Mid-Atlantic Student Colloquium on Speech, Language and Learning
Program Committee:
     ACL (2015, 2014, 2013), NAACL (2015), EMNLP (2015, 2014), COLING (2014)
     WWW (2016, 2015), AAAI (2016, 2015, 2012), KDD (2015)
     WWW Workshop on #Microposts (2016)
     ACL Workshop on Social Factors in Natural Language Processing (2016)
     EACL Workshop on Language Analysis in Social Media (2014)
Journal Reviewer:
     Transactions of the Association for Computational Linguistics (TACL)

Invited Talks
I am a big believer of collaborations and have been happy to work and co-author with:
    Colin Cherry (National Research Council Canada)
    Martin Chodorow (CUNY)
    Bill Dolan (Microsoft Research)
    Yangfeng Ji (Gatech)
    Raphael Hoffmann (U of Washington → AI2 Incubator)
    Wenjie Li (Hong Kong Polytechnic University)
    Adam Meyers (NYU)
    Courtney Napoles (JHU)
    Daniel Preoţiuc-Pietro (UPenn)
    Alan Ritter (U of Washington → Ohio State U)
    Joel Tetreault (ETS → Yahoo! Research)
    Lyle Ungar (UPenn)
    Luke Zettlemoyer (U of Washington)
    Le Zhao (CMU → Google)
    and many others ...

The members of my thesis committee are:
    Ernest Davis (NYU)
    Bill Dolan (MSR)
    Satoshi Sekine (NYU/Rakuten)
    Luke Zettlemoyer (U of Washington)

Places I interned and visited when I was a phd student:
    2012-2013, University of Washington, Seattle, WA
    Summer 2011, Microsoft Research, Redmond, WA
    Summer 2010, Amazon.com, Seattle, WA
    Spring/Fall 2010, Educational Testing Service, Princeton, NJ


When I have spare time, I enjoy arts, traveling, snowboarding, rock climbing, sailing and windsurfing.

I also made a list of the best dressed NLP researchers (2015) and (2014).