- Anthology ID:
- 2023.conll-babylm.29
- 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:
- 327–338
- Language:
- URL:
- https://aclanthology.org/2023.conll-babylm.29
- DOI:
- 10.18653/v1/2023.conll-babylm.29
- Cite (ACL):
- Omar Momen, David Arps, and Laura Kallmeyer. 2023. Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 327–338, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building (Momen et al., CoNLL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.conll-babylm.29.pdf