@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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<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.</abstract>
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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
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)
ACL