@inproceedings{costella-pessutto-etal-2020-babelenconding,
title = "{B}abel{E}nconding at {S}em{E}val-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models",
author = "Costella Pessutto, Lucas Rafael and
de Melo, Tiago and
Moreira, Viviane P. and
da Silva, Altigran",
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-1.5",
doi = "10.18653/v1/2020.semeval-1.5",
pages = "59--66",
abstract = "This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.",
}
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<abstract>This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.</abstract>
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%0 Conference Proceedings
%T BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models
%A Costella Pessutto, Lucas Rafael
%A de Melo, Tiago
%A Moreira, Viviane P.
%A da Silva, Altigran
%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 costella-pessutto-etal-2020-babelenconding
%X This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.
%R 10.18653/v1/2020.semeval-1.5
%U https://aclanthology.org/2020.semeval-1.5
%U https://doi.org/10.18653/v1/2020.semeval-1.5
%P 59-66
Markdown (Informal)
[BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models](https://aclanthology.org/2020.semeval-1.5) (Costella Pessutto et al., SemEval 2020)
ACL