@inproceedings{armendariz-etal-2020-semeval,
title = "{S}em{E}val-2020 Task 3: Graded Word Similarity in Context",
author = "Armendariz, Carlos Santos and
Purver, Matthew and
Pollak, Senja and
Ljube{\v{s}}i{\'c}, Nikola and
Ul{\v{c}}ar, Matej and
Vuli{\'c}, Ivan and
Pilehvar, Mohammad Taher",
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-1.3",
doi = "10.18653/v1/2020.semeval-1.3",
pages = "36--49",
abstract = "This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish. We received 15 submissions and 11 system description papers. A new dataset (CoSimLex) was created for evaluation in this task: it contains pairs of words, each annotated within two different contexts. Systems beat the baselines by significant margins, but few did well in more than one language or subtask. Almost every system employed a Transformer model, but with many variations in the details: WordNet sense embeddings, translation of contexts, TF-IDF weightings, and the automatic creation of datasets for fine-tuning were all used to good effect.",
}
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%0 Conference Proceedings
%T SemEval-2020 Task 3: Graded Word Similarity in Context
%A Armendariz, Carlos Santos
%A Purver, Matthew
%A Pollak, Senja
%A Ljubešić, Nikola
%A Ulčar, Matej
%A Vulić, Ivan
%A Pilehvar, Mohammad Taher
%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 armendariz-etal-2020-semeval
%X This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish. We received 15 submissions and 11 system description papers. A new dataset (CoSimLex) was created for evaluation in this task: it contains pairs of words, each annotated within two different contexts. Systems beat the baselines by significant margins, but few did well in more than one language or subtask. Almost every system employed a Transformer model, but with many variations in the details: WordNet sense embeddings, translation of contexts, TF-IDF weightings, and the automatic creation of datasets for fine-tuning were all used to good effect.
%R 10.18653/v1/2020.semeval-1.3
%U https://aclanthology.org/2020.semeval-1.3
%U https://doi.org/10.18653/v1/2020.semeval-1.3
%P 36-49
Markdown (Informal)
[SemEval-2020 Task 3: Graded Word Similarity in Context](https://aclanthology.org/2020.semeval-1.3) (Armendariz et al., SemEval 2020)
ACL
- Carlos Santos Armendariz, Matthew Purver, Senja Pollak, Nikola Ljubešić, Matej Ulčar, Ivan Vulić, and Mohammad Taher Pilehvar. 2020. SemEval-2020 Task 3: Graded Word Similarity in Context. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 36–49, Barcelona (online). International Committee for Computational Linguistics.