Conversational Question Answering

When:
Thursday, October 25, 2018, 11:00 am - 12:00 pm PSTiCal
Where:
Conf. Rm #689
This event is open to the public.
Type:
NL Seminar
Speaker:
Scott Yih (AI2)
Video Recording:
https://bluejeans.com/s/jIoDx/
Description:

Abstract: Humans seek information in a conversational manner, by asking follow-up questions for additional information based on what they have already learned. In this talk, I will first introduce the task of sequential question answering [1], which aims to fulfill user's information need by answering a series of simple, but interdependent questions regarding a given table. Treating this task as a semantic parsing problem, we developed a policy shaping mechanism that incorporates prior knowledge and an update equation that generalizes three different families of learning algorithms [2]. After that, I will then talk briefly about QuAC, a new dataset for Question Answering in Context. QuAC targets the scenario where the information source is unstructured text [3] and thus can be viewed as a conversational machine comprehension task. New, unpublished model ideas will also be discussed.

[1] Mohit Iyyer, Wen-tau Yih and Ming-Wei Chang. Search-based Neural Structured Learning for Sequential Question Answering. ACL-2017.

[2] Dipendra Misra, Ming-Wei Chang, Xiaodong He and Wen-tau Yih. Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations. EMNLP-2018.

[3] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang and Luke Zettlemoyer. QuAC: Question Answering in Context. EMNLP-2018.

Bio: Scott Wen-tau Yih is a Principal Research Scientist at Allen Institute for Artificial Intelligence (AI2). His research interests include natural language processing, machine learning and information retrieval. Yih received his Ph.D. in computer science at the University of Illinois at Urbana-Champaign. His work on joint inference using integer linear programming (ILP) has been widely adopted in the NLP community for numerous structured prediction problems. Prior to joining AI2, Yih has spent 12 years at Microsoft Research, working on a variety of projects including email spam filtering, keyword extraction and search & ad relevance. His recent work focuses on continuous representations and neural network models, with applications in knowledge base embedding, semantic parsing and question answering. Yih received the best paper award from CoNLL-2011, an outstanding paper award from ACL-2015 and has served as area co-chairs (HLT-NAACL-12, ACL-14, EMNLP-16,17,18), program co-chairs (CEAS-09, CoNLL-14) and action/associated editors (TACL, JAIR) in recent years. He is also a co-presenter for several tutorials on topics including Semantic Role Labeling (NAACL-HLT-06, AAAI-07), Deep Learning for NLP (SLT-14, NAACL-HLT-15, IJCAI-16), NLP for Precision Medicine (ACL-17, AAAI-18).

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