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A Nil-Aware Answer Extraction Framework for Question Answering

Souvik Kundu, Hwee Tou Ng


Abstract
Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.
Anthology ID:
D18-1456
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4243–4252
Language:
URL:
https://aclanthology.org/D18-1456
DOI:
10.18653/v1/D18-1456
Bibkey:
Cite (ACL):
Souvik Kundu and Hwee Tou Ng. 2018. A Nil-Aware Answer Extraction Framework for Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4243–4252, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Nil-Aware Answer Extraction Framework for Question Answering (Kundu & Ng, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1456.pdf
Video:
 https://aclanthology.org/D18-1456.mp4
Code
 nusnlp/namanda
Data
NewsQASQuAD