HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications

Xiaosong Qiao, Yinglu Li, Min Zhang, Minghan Wang, Hao Yang, Shimin Tao, Qin Ying


Abstract
This paper describes the system for the identifying Plausible Clarifications of Implicit and Underspecified Phrases. This task was set up as an English cloze task, in which clarifications are presented as possible fillers and systems have to score how well each filler plausibly fits in a given context. For this shared task, we propose our own solutions, including supervised proaches, unsupervised approaches with pretrained models, and then we use these models to build an ensemble model. Finally we get the 2nd best result in the subtask1 which is a classification task, and the 3rd best result in the subtask2 which is a regression task.
Anthology ID:
2022.semeval-1.148
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1056–1061
Language:
URL:
https://aclanthology.org/2022.semeval-1.148
DOI:
10.18653/v1/2022.semeval-1.148
Bibkey:
Cite (ACL):
Xiaosong Qiao, Yinglu Li, Min Zhang, Minghan Wang, Hao Yang, Shimin Tao, and Qin Ying. 2022. HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1056–1061, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications (Qiao et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.148.pdf