SemEval-2020 Task 3: Graded Word Similarity in Context

Carlos Santos Armendariz, Matthew Purver, Senja Pollak, Nikola Ljubešić, Matej Ulčar, Ivan Vulić, Mohammad Taher Pilehvar

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.
Anthology ID:
2020.semeval-1.3
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
36–49
Language:
URL:
https://aclanthology.org/2020.semeval-1.3
DOI:
10.18653/v1/2020.semeval-1.3
Bibkey:
Cite (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.
Cite (Informal):
SemEval-2020 Task 3: Graded Word Similarity in Context (Armendariz et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2020.semeval-1.3.pdf
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