@inproceedings{qiao-etal-2022-hw,
title = "{HW}-{TSC} at {S}em{E}val-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications",
author = "Qiao, Xiaosong and
Li, Yinglu and
Zhang, Min and
Wang, Minghan and
Yang, Hao and
Tao, Shimin and
Ying, Qin",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.148",
doi = "10.18653/v1/2022.semeval-1.148",
pages = "1056--1061",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications
%A Qiao, Xiaosong
%A Li, Yinglu
%A Zhang, Min
%A Wang, Minghan
%A Yang, Hao
%A Tao, Shimin
%A Ying, Qin
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F qiao-etal-2022-hw
%X 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.
%R 10.18653/v1/2022.semeval-1.148
%U https://aclanthology.org/2022.semeval-1.148
%U https://doi.org/10.18653/v1/2022.semeval-1.148
%P 1056-1061
Markdown (Informal)
[HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications](https://aclanthology.org/2022.semeval-1.148) (Qiao et al., SemEval 2022)
ACL