@inproceedings{gangi-reddy-etal-2020-answer,
title = "Answer Span Correction in Machine Reading Comprehension",
author = "Gangi Reddy, Revanth and
Sultan, Md Arafat and
Sarioglu Kayi, Efsun and
Zhang, Rong and
Castelli, Vittorio and
Sil, Avi",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.226/",
doi = "10.18653/v1/2020.findings-emnlp.226",
pages = "2496--2501",
abstract = "Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the {\textquotedblleft}answerability{\textquotedblright} of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions. We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation."
}
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<abstract>Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the “answerability” of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions. We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation.</abstract>
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%0 Conference Proceedings
%T Answer Span Correction in Machine Reading Comprehension
%A Gangi Reddy, Revanth
%A Sultan, Md Arafat
%A Sarioglu Kayi, Efsun
%A Zhang, Rong
%A Castelli, Vittorio
%A Sil, Avi
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gangi-reddy-etal-2020-answer
%X Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the “answerability” of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions. We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation.
%R 10.18653/v1/2020.findings-emnlp.226
%U https://aclanthology.org/2020.findings-emnlp.226/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.226
%P 2496-2501
Markdown (Informal)
[Answer Span Correction in Machine Reading Comprehension](https://aclanthology.org/2020.findings-emnlp.226/) (Gangi Reddy et al., Findings 2020)
ACL
- Revanth Gangi Reddy, Md Arafat Sultan, Efsun Sarioglu Kayi, Rong Zhang, Vittorio Castelli, and Avi Sil. 2020. Answer Span Correction in Machine Reading Comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2496–2501, Online. Association for Computational Linguistics.