Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension

Liang Wang; Meng Sun; Wei Zhao; Kewei Shen; Jingming Liu

doi:10.18653/v1/S18-1120

Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension

Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, Jingming Liu

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Abstract

This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet. As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc.

Anthology ID:: S18-1120
Volume:: Proceedings of the 12th International Workshop on Semantic Evaluation
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 758–762
Language:
URL:: https://aclanthology.org/S18-1120/
DOI:: 10.18653/v1/S18-1120
Bibkey:
Cite (ACL):: Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, and Jingming Liu. 2018. Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 758–762, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension (Wang et al., SemEval 2018)
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PDF:: https://aclanthology.org/S18-1120.pdf

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@inproceedings{wang-etal-2018-yuanfudao,
    title = "Yuanfudao at {S}em{E}val-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension",
    author = "Wang, Liang  and
      Sun, Meng  and
      Zhao, Wei  and
      Shen, Kewei  and
      Liu, Jingming",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1120/",
    doi = "10.18653/v1/S18-1120",
    pages = "758--762",
    abstract = "This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet. As a result, our system achieves state-of-the-art performance with 83.95{\%} accuracy on the official test data. Code is publicly available at \url{https://github.com/intfloat/commonsense-rc}."
}

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%0 Conference Proceedings
%T Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
%A Wang, Liang
%A Sun, Meng
%A Zhao, Wei
%A Shen, Kewei
%A Liu, Jingming
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wang-etal-2018-yuanfudao
%X This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet. As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc.
%R 10.18653/v1/S18-1120
%U https://aclanthology.org/S18-1120/
%U https://doi.org/10.18653/v1/S18-1120
%P 758-762

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Markdown (Informal)

[Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension](https://aclanthology.org/S18-1120/) (Wang et al., SemEval 2018)

Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension (Wang et al., SemEval 2018)

ACL

Liang Wang, Meng Sun, Wei Zhao, Kewei Shen, and Jingming Liu. 2018. Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 758–762, New Orleans, Louisiana. Association for Computational Linguistics.