CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations

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Authors Roman Lyapin, Martin Pömsl
Journal/Conference Name arXiv preprint
Paper Category
Paper Abstract This paper describes the winning contribution to SemEval-2020 Task 1 Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations can in some cases improve performance, suggesting that both contain unique relevant information.
Date of publication 2020
Code Programming Language Python

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