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

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

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
Comment

Copyright Researcher 2022