No clues good clues: out of context Lexical Relation Classification

Lucia Pitarch, Jordi Bernad, Lacramioara Dranca, Carlos Bobed Lisbona, Jorge Gracia

Abstract
The accurate prediction of lexical relations between words is a challenging task in Natural Language Processing (NLP). The most recent advances in this direction come with the use of pre-trained language models (PTLMs). A PTLM typically needs “well-formed” verbalized text to interact with it, either to fine-tune it or to exploit it. However, there are indications that commonly used PTLMs already encode enough linguistic knowledge to allow the use of minimal (or none) textual context for some linguistically motivated tasks, thus notably reducing human effort, the need for data pre-processing, and favoring techniques that are language neutral since do not rely on syntactic structures. In this work, we explore this idea for the tasks of lexical relation classification (LRC) and graded Lexical Entailment (LE). After fine-tuning PTLMs for LRC with different verbalizations, our evaluation results show that very simple prompts are competitive for LRC and significantly outperform graded LE SoTA. In order to gain a better insight into this phenomenon, we perform a number of quantitative statistical analyses on the results, as well as a qualitative visual exploration based on embedding projections.
Anthology ID:
2023.acl-long.308
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5607–5625
Language:
URL:
https://aclanthology.org/2023.acl-long.308
DOI:
10.18653/v1/2023.acl-long.308
Bibkey:
Cite (ACL):
Lucia Pitarch, Jordi Bernad, Lacramioara Dranca, Carlos Bobed Lisbona, and Jorge Gracia. 2023. No clues good clues: out of context Lexical Relation Classification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5607–5625, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
No clues good clues: out of context Lexical Relation Classification (Pitarch et al., ACL 2023)
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