@inproceedings{ding-etal-2020-hit,
title = "{HIT}-{SCIR} at {S}em{E}val-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection",
author = "Ding, Xiao and
Hao, Dingkui and
Zhang, Yuewei and
Liao, Kuo and
Li, Zhongyang and
Qin, Bing and
Liu, Ting",
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-1.43",
doi = "10.18653/v1/2020.semeval-1.43",
pages = "354--360",
abstract = "We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today{'}s AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning. In this task, we dedicate to detecting causation, especially counterfactuals from texts. We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data. Our team HIT-SCIR wins the first place (1st) in Sub-task 1 {---} Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 {---} Detecting Antecedent and Consequence. In this paper we provide a detailed description of the approach, as well as the results obtained in this task.",
}
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<abstract>We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today’s AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning. In this task, we dedicate to detecting causation, especially counterfactuals from texts. We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data. Our team HIT-SCIR wins the first place (1st) in Sub-task 1 — Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 — Detecting Antecedent and Consequence. In this paper we provide a detailed description of the approach, as well as the results obtained in this task.</abstract>
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%0 Conference Proceedings
%T HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
%A Ding, Xiao
%A Hao, Dingkui
%A Zhang, Yuewei
%A Liao, Kuo
%A Li, Zhongyang
%A Qin, Bing
%A Liu, Ting
%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 ding-etal-2020-hit
%X We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today’s AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning. In this task, we dedicate to detecting causation, especially counterfactuals from texts. We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data. Our team HIT-SCIR wins the first place (1st) in Sub-task 1 — Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 — Detecting Antecedent and Consequence. In this paper we provide a detailed description of the approach, as well as the results obtained in this task.
%R 10.18653/v1/2020.semeval-1.43
%U https://aclanthology.org/2020.semeval-1.43
%U https://doi.org/10.18653/v1/2020.semeval-1.43
%P 354-360
Markdown (Informal)
[HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection](https://aclanthology.org/2020.semeval-1.43) (Ding et al., SemEval 2020)
ACL