@inproceedings{wang-etal-2018-multi-passage,
title = "Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification",
author = "Wang, Yizhong and
Liu, Kai and
Liu, Jing and
He, Wei and
Lyu, Yajuan and
Wu, Hua and
Li, Sujian and
Wang, Haifeng",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1178",
doi = "10.18653/v1/P18-1178",
pages = "1918--1927",
abstract = "Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.",
}
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<abstract>Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.</abstract>
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%0 Conference Proceedings
%T Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
%A Wang, Yizhong
%A Liu, Kai
%A Liu, Jing
%A He, Wei
%A Lyu, Yajuan
%A Wu, Hua
%A Li, Sujian
%A Wang, Haifeng
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wang-etal-2018-multi-passage
%X Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.
%R 10.18653/v1/P18-1178
%U https://aclanthology.org/P18-1178
%U https://doi.org/10.18653/v1/P18-1178
%P 1918-1927
Markdown (Informal)
[Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification](https://aclanthology.org/P18-1178) (Wang et al., ACL 2018)
ACL
- Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, and Haifeng Wang. 2018. Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1918–1927, Melbourne, Australia. Association for Computational Linguistics.