Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets

Pranav A, Yan Cong, Emmanuele Chersoni, Yu Yin Hsu, Alessandro Lenci

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language and a limited number of models. In our study, we present a comparative evaluation of static and contextualized distributional models for Mandarin Chinese, focusing on a range of intrinsic tasks. Our results reveal that static models remain stronger for some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and in the categorization of words into abstract semantic classes.
Original languageEnglish
Title of host publicationProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
EditorsNicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
PublisherAssociation for Computational Linguistics (ACL)
Pages3610-3627
ISBN (Electronic)978-2-493814-10-4
Publication statusPublished - May 2024
EventThe 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation - Turin, Italy
Duration: 20 May 202425 May 2024
https://lrec-coling-2024.org/

Conference

ConferenceThe 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Abbreviated titleLREC-COLING 2024
Country/TerritoryItaly
CityTurin
Period20/05/2425/05/24
Internet address

Keywords

  • Distributional Semantic Models
  • Mandarin Chinese
  • Semantic Similarity
  • Transformers

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