NDLI: Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning

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Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning

Content Provider	ACM Digital Library
Author	Kumar, Vineet Joshi, Sachindra
Abstract	Intelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter incomplete follow-up questions. The incomplete follow-up questions only make sense when seen in conjunction with the conversation context: the previous question and answer. Thus, IQA and IPA systems need to utilize the conversation context in order to handle the incomplete follow-up questions and generate an appropriate response. In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context). We can train our system using only a small labeled dataset (with only a few thousand conversations), by decomposing the original problem into two simpler and independent problems. The first problem focuses solely on selecting the candidate complete questions from a library of question templates (built offline using the small labeled conversations dataset). In the second problem, we re-rank the selected candidate questions using a neural language model (trained on millions of unlabelled questions independently). Our system can achieve a BLEU score of 42.91, as compared to 29.11 using an existing generation based approach. We further demonstrate the utility of our system as a plug-in module to an existing QA pipeline. Our system when added as a plug-in module, enables Siri to achieve an improvement of 131.57% in answering incomplete follow-up questions.
Starting Page	705
Ending Page	714
Page Count	10
File Format	PDF
ISBN	9781450350228
DOI	10.1145/3077136.3080801
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interactive question answering Query reformulation Intelligent personal assistants Follow-up question resolution Retrieval based sequence to sequence learning
Content Type	Text
Resource Type	Article

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