@inproceedings{khashabi-etal-2017-learning,
title = "Learning What is Essential in Questions",
author = "Khashabi, Daniel and
Khot, Tushar and
Sabharwal, Ashish and
Roth, Dan",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1010",
doi = "10.18653/v1/K17-1010",
pages = "80--89",
abstract = "Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers. We illustrate the importance of essential question terms by showing that humans{'} ability to answer questions drops significantly when essential terms are eliminated from questions. We then develop a classifier that reliably (90{\%} mean average precision) identifies and ranks essential terms in questions. Finally, we use the classifier to demonstrate that the notion of question term essentiality allows state-of-the-art QA solver for elementary-level science questions to make better and more informed decisions,improving performance by up to 5{\%}.We also introduce a new dataset of over 2,200 crowd-sourced essential terms annotated science questions.",
}
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%0 Conference Proceedings
%T Learning What is Essential in Questions
%A Khashabi, Daniel
%A Khot, Tushar
%A Sabharwal, Ashish
%A Roth, Dan
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F khashabi-etal-2017-learning
%X Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers. We illustrate the importance of essential question terms by showing that humans’ ability to answer questions drops significantly when essential terms are eliminated from questions. We then develop a classifier that reliably (90% mean average precision) identifies and ranks essential terms in questions. Finally, we use the classifier to demonstrate that the notion of question term essentiality allows state-of-the-art QA solver for elementary-level science questions to make better and more informed decisions,improving performance by up to 5%.We also introduce a new dataset of over 2,200 crowd-sourced essential terms annotated science questions.
%R 10.18653/v1/K17-1010
%U https://aclanthology.org/K17-1010
%U https://doi.org/10.18653/v1/K17-1010
%P 80-89
Markdown (Informal)
[Learning What is Essential in Questions](https://aclanthology.org/K17-1010) (Khashabi et al., CoNLL 2017)
ACL
- Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2017. Learning What is Essential in Questions. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 80–89, Vancouver, Canada. Association for Computational Linguistics.