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

• URL: http://arxiv.org/abs/2005.06602v3
• Date: Tue, 6 Oct 2020 13:50:47 GMT
• Title: CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
• Authors: Martin P\"omsl (Osnabr\"uck University) and Roman Lyapin (Cogent Labs Inc.)
• Abstract summary: 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 increases performance on some datasets while decreasing performance on others.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 increases performance on some datasets while decreasing performance on others.

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