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.- Anthology ID:
- 2022.coling-1.283
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3197–3209
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.283
- DOI:
- Cite (ACL):
- Tommaso Caselli, Irene Dini, and Felice Dell’Orletta. 2022. How about Time? Probing a Multilingual Language Model for Temporal Relations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3197–3209, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- How about Time? Probing a Multilingual Language Model for Temporal Relations (Caselli et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.283.pdf
- Code
- irenedini/tlink_probing