BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models

• URL: http://arxiv.org/abs/2007.14128v1
• Date: Tue, 28 Jul 2020 11:16:11 GMT
• Title: BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
• Authors: Martin Fajcik, Josef Jon, Martin Docekal, Pavel Smrz
• Abstract summary: This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual. We found RoBERTa LRM to perform the best in both subtasks.
• Score: 6.853018135783218
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text (Subtask 2). We experimented with various state-of-the-art language representation models (LRMs). We found RoBERTa LRM to perform the best in both subtasks. We achieved the first place in both exact match and F1 for Subtask 2 and ranked second for Subtask 1.

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