Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies

Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, Paula Buttery


Abstract
Curriculum Learning has been a popular strategy to improve the cognitive plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge. However, it has not led to considerable improvements over non-curriculum models. We assess whether theoretical linguistic acquisition theories can be used to specify more fine-grained curriculum learning strategies, creating age-ordered corpora of Child-Directed Speech for four typologically distant language families to implement SSLMs and acquisition-inspired curricula cross-lingually. Comparing the success of three objective curricula (Growing, Inwards & MMM) that precisely replicate the predictions of acquisition theories on a standard SSLM architecture, we find fine-grained acquisition-inspired curricula can outperform non-curriculum baselines and performance benefits of curricula strategies in SSLMs can be derived by specifying fine-grained language-specific curricula that precisely replicate language acquisition theories.
Anthology ID:
2024.conll-babylm.15
Volume:
The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning
Month:
November
Year:
2024
Address:
Miami, FL, USA
Editors:
Michael Y. Hu, Aaron Mueller, Candace Ross, Adina Williams, Tal Linzen, Chengxu Zhuang, Leshem Choshen, Ryan Cotterell, Alex Warstadt, Ethan Gotlieb Wilcox
Venues:
CoNLL | BabyLM | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
174–188
Language:
URL:
https://aclanthology.org/2024.conll-babylm.15/
DOI:
Bibkey:
Cite (ACL):
Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, and Paula Buttery. 2024. Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies. In The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning, pages 174–188, Miami, FL, USA. Association for Computational Linguistics.
Cite (Informal):
Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies (Salhan et al., CoNLL-BabyLM 2024)
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PDF:
https://aclanthology.org/2024.conll-babylm.15.pdf