How About Time? Probing a Multilingual Language Model for Temporal Relations

Tommaso Caselli, Irene Dini, Felice Dell'Orletta

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Abstract

This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages. Results show an advantage of contextualized embeddings over static ones and a detrimen- tal role of sentence level embeddings. While obtaining competitive results against state-of-the-art systems, our probes indicate a lack of suitable encoded information to properly address this task.
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Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Computational Linguistics
Place of PublicationGyeongju, Republic of Korea
PublisherInternational Committee on Computational Linguistics (ICCL)
Pages3197-3209
Number of pages13
Publication statusPublished - 2022
Event29th International Conference on Computational Linguistics - Gyeongju, Korea, Republic of
Duration: 12-Oct-202217-Oct-2022

Conference

Conference29th International Conference on Computational Linguistics
Abbreviated titleCOLING 2022
Country/TerritoryKorea, Republic of
CityGyeongju
Period12/10/202217/10/2022

Keywords

  • probing
  • temporal relation
  • event
  • Outstanding Paper - COLING 2022

    Caselli, Tommaso (Recipient), Dini, Irene (Recipient) & Dell'Orletta, Felice (Recipient), 2022

    Prize: National/international honourAcademic

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