Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection

Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova


Abstract
This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection. The proposed method is based on clustering of BERT contextual embeddings, followed by a comparison of cluster distributions across time. The best results were obtained by an ensemble of this method and static Word2Vec embeddings. According to the official results, our approach proved the best for Latin in Subtask 2.
Anthology ID:
2020.semeval-1.6
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
67–73
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.6
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.6.pdf