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.- Anthology ID:
- 2020.semeval-1.21
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 180–186
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.21
- DOI:
- 10.18653/v1/2020.semeval-1.21
- Cite (ACL):
- Martin Pömsl and Roman Lyapin. 2020. CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 180–186, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations (Pömsl & Lyapin, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.semeval-1.21.pdf
- Code
- mpoemsl/circe + additional community code