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  New, unpublished model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.