Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2018

Abstract

In recent years researchers have achieved considerable success applyingneural network methods to question answering (QA). These approaches haveachieved state of the art results in simplified closed-domain settings such asthe SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selectedpassage, from which the answer to a given question may be extracted. Morerecently, researchers have begun to tackle open-domain QA, in which the modelis given a question and access to a large corpus (e.g., wikipedia) instead of apre-selected passage (Chen et al., 2017a). This setting is more complex as itrequires large-scale search for relevant passages by an information retrievalcomponent, combined with a reading comprehension model that "reads" thepassages to generate an answer to the question. Performance in this settinglags considerably behind closed-domain performance. In this paper, we present anovel open-domain QA system called Reinforced Ranker-Reader (R3), based ontwo algorithmic innovations. First, we propose a new pipeline for open-domainQA with a Ranker component, which learns to rank retrieved passages in terms oflikelihood of generating the ground-truth answer to a given question. Second,we propose a novel method that jointly trains the Ranker along with ananswer-generation Reader model, based on reinforcement learning. We reportextensive experimental results showing that our method significantly improveson the state of the art for multiple open-domain QA datasets.

Keywords

Answer extraction, Ground truth, Neural network method, Open domain question answering, Question Answering, Reading comprehension

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Palo Alto, CA, 2018 February 2-7

First Page

5981

Last Page

5998

ISBN

9781577358008

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

Authors

Additional URL

https://arxiv.org/abs/1709.00023

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