@inproceedings{wan-huang-2020-kalm,
title = "{K}a{LM} at {S}em{E}val-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation",
author = "Wan, Jiajing and
Huang, Xinting",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.67",
doi = "10.18653/v1/2020.semeval-1.67",
pages = "543--550",
abstract = "This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.",
}
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<abstract>This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.</abstract>
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%0 Conference Proceedings
%T KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation
%A Wan, Jiajing
%A Huang, Xinting
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F wan-huang-2020-kalm
%X This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.
%R 10.18653/v1/2020.semeval-1.67
%U https://aclanthology.org/2020.semeval-1.67
%U https://doi.org/10.18653/v1/2020.semeval-1.67
%P 543-550
Markdown (Informal)
[KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation](https://aclanthology.org/2020.semeval-1.67) (Wan & Huang, SemEval 2020)
ACL