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        <title>BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense</title>
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            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
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            <namePart type="given">Xiaodan</namePart>
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            <namePart type="given">Alexis</namePart>
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            <namePart type="given">Jonathan</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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    <abstract>We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation. We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs. We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss. In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low. We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle. We performed an error and dataset analysis for all subtasks and we present our findings.</abstract>
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%0 Conference Proceedings
%T BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense
%A Jon, Josef
%A Fajcik, Martin
%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 jon-etal-2020-fit
%X We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation. We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs. We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss. In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low. We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle. We performed an error and dataset analysis for all subtasks and we present our findings.
%R 10.18653/v1/2020.semeval-1.46
%U https://aclanthology.org/2020.semeval-1.46/
%U https://doi.org/10.18653/v1/2020.semeval-1.46
%P 374-390

Markdown (Informal)

[BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense](https://aclanthology.org/2020.semeval-1.46/) (Jon et al., SemEval 2020)

• BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense (Jon et al., SemEval 2020)

ACL

• Josef Jon, Martin Fajcik, Martin Docekal, and Pavel Smrz. 2020. BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 374–390, Barcelona (online). International Committee for Computational Linguistics.