@inproceedings{fajcik-etal-2020-fit,
title = "{BUT}-{FIT} at {S}em{E}val-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models",
author = "Fajcik, Martin and
Jon, Josef and
Docekal, Martin and
Smrz, Pavel",
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-1.53",
doi = "10.18653/v1/2020.semeval-1.53",
pages = "437--444",
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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<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.</abstract>
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%0 Conference Proceedings
%T BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models
%A Fajcik, Martin
%A Jon, Josef
%A Docekal, Martin
%A Smrz, Pavel
%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 fajcik-etal-2020-fit
%X 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.
%R 10.18653/v1/2020.semeval-1.53
%U https://aclanthology.org/2020.semeval-1.53
%U https://doi.org/10.18653/v1/2020.semeval-1.53
%P 437-444
Markdown (Informal)
[BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models](https://aclanthology.org/2020.semeval-1.53) (Fajcik et al., SemEval 2020)
ACL