@inproceedings{chen-etal-2020-multi,
title = "Multi-choice Relational Reasoning for Machine Reading Comprehension",
author = "Chen, Wuya and
Quan, Xiaojun and
Kit, Chunyu and
Min, Zhengcheng and
Wang, Jiahai",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.567",
doi = "10.18653/v1/2020.coling-main.567",
pages = "6448--6458",
abstract = "This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children{'}s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.",
}
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<abstract>This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR²) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children’s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.</abstract>
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%0 Conference Proceedings
%T Multi-choice Relational Reasoning for Machine Reading Comprehension
%A Chen, Wuya
%A Quan, Xiaojun
%A Kit, Chunyu
%A Min, Zhengcheng
%A Wang, Jiahai
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F chen-etal-2020-multi
%X This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR²) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children’s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.
%R 10.18653/v1/2020.coling-main.567
%U https://aclanthology.org/2020.coling-main.567
%U https://doi.org/10.18653/v1/2020.coling-main.567
%P 6448-6458
Markdown (Informal)
[Multi-choice Relational Reasoning for Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.567) (Chen et al., COLING 2020)
ACL