Wei Xu     

[phonetic pronunciation: way shoo ]

Assistant Professor
Department of Computer Science and Engineering
The Ohio State University
   weixu@cse.ohio-state.edu
   495 Dreese Lab (2015 Neil Ave, Columbus, OH 43210)

My research lies at the intersections of machine learning, natural language processing, and social media. I focus on designing algorithms for learning semantics from large data for natural language understanding, and generation in particular with stylistic variations. I received the NSF CRII Award and CrowdFlower AI for Everyone Award, as well as research funds from DARPA. Previously, I was a postdoctoral researcher at the University of Pennsylvania. I received my PhD in Computer Science from New York University where I was a MacCracken Fellow, MS and BS from Tsinghua University.

I am a workshop chair for ACL 2017, an area chair for EMNLP 2018 (social media area), COLING 2018 (semantics area), EMNLP 2016 (generation area), and the publicity chair for NAACL 2016 and 2018. I also created the Twitter API tutorial and a new course on Social Media and Text Analytics.

I am looking to recruit one or two new PhD students each year. Here is a note to prospective students.
What's New
  May 11, San Francisco - talk at Twitter HQ
  May 14, Menlo Park - talk at Facebook HQ
  June 1-5, New Orleans - NAACL conference
  June 6, Chicago - invited talk at Midwest Machine Learning Symposium
Talk on Twitter Paraphrase @ NAACL 2015

Talk on Text Simplification @ EMNLP 2015
Teaching
CSE 5522 Artificial Intelligence II: Advanced Techniques (Spring 2018; Fall 2018)
CSE 5539 Social Media and Text Analytics (Fall 2017; Fall 2016)
CSE 5525 Speech and Language Processing (Spring 2017)

Students
Current Students:
    Wuwei Lan (PhD student, 2016 -- ; semantics/deep learning EMNLP'17 NAACL'18a COLING'18)
    Mounica Maddela (PhD student, 2017 -- ; stylistics)
    Serena Davis (Masters student, 2018 -- ; social media)
    Lillian Chow (Undergraduate, summer 2018)
    Jiawei Gu (Undergraduate, summer 2018)
    Sydney Lee (Undergraduate, summer 2018)

Other Students:
    Chaitanya Kulkarni (PhD student; robotic instructions NAACL'18b - advisor: Raghu Machiraju)
    Jeniya Tabassum (PhD student; information extraction EMNLP'16 - advisor: Alan Ritter)

Former Students:
    Maria Pershina (PhD student @NYU; information extraction ACL'14 - now Goldman Sachs NYC)
    Jim Chen (Undergraduate @UPenn; crowdsourcing HCOMP'14 TACL'16 - now PhD University of Washington)
    Mingkun Gao (Masters student @UPenn; machine translation NAACL'15 - now PhD UIUC)
    Ray Lei (Undergraduate @UPenn; crowdsourcing HCOMP'14 - now Microsoft Redmond)
    Siyu Qiu (Masters student @UPenn; semantics EMNLP'17 - now Hulu LA)
    Wenchao Du (Undergraduate @UWaterloo; dialog AAAI'17 SAP - now Master CMU LTI)
    Piyush Ghai (Masters student @OSU; semantics)
    Pravar Mahajan (Masters student @OSU; social media)

Research Highlights

Natural Language Understanding / Semantics

We design machine learning algorithms to extract semantic or structured knowledge from large volumes of data. We have a series of work on learning web-scale paraphrases from Twitter that can 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”) [BUCC'13] [SemEval'15]. It is difficult to capture such lexically divergent paraphrases by the conventional similarity-based approaches. We design large-scale data [EMNLP'17], neural network models for sentence pair modeling [NAACL'18a] and multi-instance learning models [TACL'14] [EMNLP'16], which jointly infers latent word-sentence relations.

Natural Language Generation / Stylistics

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 text-to-text generation framework, building on top of machine translation technologies. My recent work uncovered multiple serious problems in text simplification [TACL'15] 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 [TACL'16]. I am also very interested in paraphrases of different language styles (e.g. historic ↔ modern [COLING'12], non-standard ↔ standard [BUCC'13], feminine ↔ masculine [AAAI'16]).

Publications
Professional Service
Workshop Chair:   ACL (2017)
Area Chair:   COLING (2018), EMNLP (2018, 2016)
Publicity Chair:   NAACL (2018, 2016)
Organizer:
     - Workshop on Noisy User-generated Text (W-NUT) at ACL 2015, COLING 2016, EMNLP 2017 & 2018
     - SemEval 2015 shared-task: Paraphrases and Semantic Similarity in Twitter
     - 2016 Mid-Atlantic Student Colloquium on Speech, Language and Learning
Program Committee:
     ACL (2018, 2017, 2015, 2014, 2013), NAACL (2018, 2015), EMNLP (2017, 2016, 2015, 2014), COLING (2016, 2014)
     WWW (2016, 2015), AAAI (2016, 2015, 2012), KDD (2015)
Journal Reviewer:
     Transactions of the Association for Computational Linguistics (TACL)
     Journal of Artificial Intelligence Research (JAIR)

Invited Talks
Miscellaneous

When I have spare time, I enjoy traveling, swimming and snowboarding.

I also made a list of the best dressed NLP researchers (2016/17) , (2015) and (2014).