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

Tommaso Caselli, Irene Dini, Felice Dell’Orletta


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.283.pdf
Code
 irenedini/tlink_probing