BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition

Miyu Oba, Akari Haga, Akiyo Fukatsu, Yohei Oseki


Anthology ID:
2023.conll-babylm.25
Volume:
Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
Month:
December
Year:
2023
Address:
Singapore
Editors:
Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell
Venue:
CoNLL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
290–297
Language:
URL:
https://aclanthology.org/2023.conll-babylm.25
DOI:
10.18653/v1/2023.conll-babylm.25
Bibkey:
Cite (ACL):
Miyu Oba, Akari Haga, Akiyo Fukatsu, and Yohei Oseki. 2023. BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 290–297, Singapore. Association for Computational Linguistics.
Cite (Informal):
BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition (Oba et al., CoNLL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.conll-babylm.25.pdf