KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation

Jiajing Wan, Xinting Huang


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.
Anthology ID:
2020.semeval-1.67
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
543–550
Language:
URL:
https://aclanthology.org/2020.semeval-1.67
DOI:
10.18653/v1/2020.semeval-1.67
Bibkey:
Cite (ACL):
Jiajing Wan and Xinting Huang. 2020. KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 543–550, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation (Wan & Huang, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.67.pdf
Code
 huangxt39/KaLM
Data
CommonsenseQA