BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
July 28, 2020 ยท Declared Dead ยท ๐ International Workshop on Semantic Evaluation
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Authors
Martin Fajcik, Josef Jon, Martin Docekal, Pavel Smrz
arXiv ID
2007.14128
Category
cs.CL: Computation & Language
Cross-listed
cs.LG,
stat.ML
Citations
11
Venue
International Workshop on Semantic Evaluation
Last Checked
5 months ago
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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