- 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
- Bibkey:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.conll-babylm.29.pdf
Export citation
@inproceedings{momen-etal-2023-increasing,
title = "Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building",
author = "Momen, Omar and
Arps, David and
Kallmeyer, Laura",
editor = "Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan",
booktitle = "Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-babylm.29",
doi = "10.18653/v1/2023.conll-babylm.29",
pages = "327--338",
}
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%0 Conference Proceedings %T Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building %A Momen, Omar %A Arps, David %A Kallmeyer, Laura %Y Warstadt, Alex %Y Mueller, Aaron %Y Choshen, Leshem %Y Wilcox, Ethan %Y Zhuang, Chengxu %Y Ciro, Juan %Y Mosquera, Rafael %Y Paranjabe, Bhargavi %Y Williams, Adina %Y Linzen, Tal %Y Cotterell, Ryan %S Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning %D 2023 %8 December %I Association for Computational Linguistics %C Singapore %F momen-etal-2023-increasing %R 10.18653/v1/2023.conll-babylm.29 %U https://aclanthology.org/2023.conll-babylm.29 %U https://doi.org/10.18653/v1/2023.conll-babylm.29 %P 327-338
Markdown (Informal)
[Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building](https://aclanthology.org/2023.conll-babylm.29) (Momen et al., CoNLL 2023)
- Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building (Momen et al., CoNLL 2023)
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.