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        <title>Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension</title>
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        <namePart type="given">Hongyu</namePart>
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    <abstract>Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.</abstract>
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%0 Conference Proceedings
%T Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
%A Lin, Hongyu
%A Sun, Le
%A Han, Xianpei
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lin-etal-2017-reasoning
%X Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.
%R 10.18653/v1/D17-1216
%U https://aclanthology.org/D17-1216
%U https://doi.org/10.18653/v1/D17-1216
%P 2032-2043

Markdown (Informal)

[Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension](https://aclanthology.org/D17-1216) (Lin et al., EMNLP 2017)

• Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension (Lin et al., EMNLP 2017)

ACL

• Hongyu Lin, Le Sun, and Xianpei Han. 2017. Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2032–2043, Copenhagen, Denmark. Association for Computational Linguistics.