Linguistic Profiling of a Neural Language Model

Alessio Miaschi, Dominique Brunato, Felice Dell’Orletta, Giulia Venturi


Abstract
In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT’s capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.
Anthology ID:
2020.coling-main.65
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
745–756
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.65
DOI:
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http://aclanthology.lst.uni-saarland.de/2020.coling-main.65.pdf