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        <title>Explicit Utilization of General Knowledge in Machine Reading Comprehension</title>
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        <namePart type="given">Chao</namePart>
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    <abstract>To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1219</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Explicit Utilization of General Knowledge in Machine Reading Comprehension
%A Wang, Chao
%A Jiang, Hui
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-jiang-2019-explicit
%X To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.
%R 10.18653/v1/P19-1219
%U https://aclanthology.org/P19-1219
%U https://doi.org/10.18653/v1/P19-1219
%P 2263-2272

Markdown (Informal)

[Explicit Utilization of General Knowledge in Machine Reading Comprehension](https://aclanthology.org/P19-1219) (Wang & Jiang, ACL 2019)

• Explicit Utilization of General Knowledge in Machine Reading Comprehension (Wang & Jiang, ACL 2019)

ACL

• Chao Wang and Hui Jiang. 2019. Explicit Utilization of General Knowledge in Machine Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2263–2272, Florence, Italy. Association for Computational Linguistics.