HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
Xiao Ding, Dingkui Hao, Yuewei Zhang, Kuo Liao, Zhongyang Li, Bing Qin, Ting Liu · 2020
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 1 .