Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT

Wei Bao, Hongshu Che, Jiandong Zhang


Abstract
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years. Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020. We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT). Our team will go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1.
Anthology ID:
2020.semeval-1.38
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
301–306
Language:
URL:
https://aclanthology.org/2020.semeval-1.38
DOI:
10.18653/v1/2020.semeval-1.38
Bibkey:
Cite (ACL):
Wei Bao, Hongshu Che, and Jiandong Zhang. 2020. Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 301–306, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT (Bao et al., SemEval 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.semeval-1.38.pdf