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

Tommaso Caselli, Irene Dini, Felice Dell'Orletta

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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)
Number of pages13
Publication statusPublished - 2022
Event29th International Conference on Computational Linguistics - Gyeongju, Korea, Republic of
Duration: 12-Oct-202217-Oct-2022


Conference29th International Conference on Computational Linguistics
Abbreviated titleCOLING 2022
Country/TerritoryKorea, Republic of


  • 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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