Re-solve it: simulating the acquisition of core semantic competences from small data

Aurélie Herbelot

Abstract
Many tasks are considered to be ‘solved’ in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition. I illustrate this by coming back to the notion of semantic competence, which includes basic linguistic skills encompassing both referential phenomena and generic knowledge, in particular a) the ability to denote, b) the mastery of the lexicon, or c) the ability to model one’s language use on others. Even though each of those faculties has been extensively tested individually, there is still no computational model that would account for their joint acquisition under the conditions experienced by a human. In this paper, I focus on one particular aspect of this problem: the amount of linguistic data available to the child or machine. I show that given the first competence mentioned above (a denotation function), the other two can in fact be learned from very limited data (2.8M token), reaching state-of-the-art performance. I argue that both the nature of the data and the way it is presented to the system matter to acquisition.
Anthology ID:
2020.conll-1.27
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Editors:
Raquel Fernández, Tal Linzen
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
344–354
Language:
URL:
https://aclanthology.org/2020.conll-1.27
DOI:
10.18653/v1/2020.conll-1.27
Bibkey:
Cite (ACL):
Aurélie Herbelot. 2020. Re-solve it: simulating the acquisition of core semantic competences from small data. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 344–354, Online. Association for Computational Linguistics.
Cite (Informal):
Re-solve it: simulating the acquisition of core semantic competences from small data (Herbelot, CoNLL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.conll-1.27.pdf
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