@inproceedings{ghosh-etal-2024-morphology,
title = "A Morphology-Based Investigation of Positional Encodings",
author = "Ghosh, Poulami and
Vashishth, Shikhar and
Dabre, Raj and
Bhattacharyya, Pushpak",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1170",
pages = "21035--21045",
abstract = "Contemporary deep learning models effectively handle languages with diverse morphology despite not being directly integrated into them. Morphology and word order are closely linked, with the latter incorporated into transformer-based models through positional encodings. This prompts a fundamental inquiry: Is there a correlation between the morphological complexity of a language and the utilization of positional encoding in pre-trained language models? In pursuit of an answer, we present the first study addressing this question, encompassing 22 languages and 5 downstream tasks. Our findings reveal that the importance of positional encoding diminishes with increasing morphological complexity in languages. Our study motivates the need for a deeper understanding of positional encoding, augmenting them to better reflect the different languages under consideration.",
}
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<abstract>Contemporary deep learning models effectively handle languages with diverse morphology despite not being directly integrated into them. Morphology and word order are closely linked, with the latter incorporated into transformer-based models through positional encodings. This prompts a fundamental inquiry: Is there a correlation between the morphological complexity of a language and the utilization of positional encoding in pre-trained language models? In pursuit of an answer, we present the first study addressing this question, encompassing 22 languages and 5 downstream tasks. Our findings reveal that the importance of positional encoding diminishes with increasing morphological complexity in languages. Our study motivates the need for a deeper understanding of positional encoding, augmenting them to better reflect the different languages under consideration.</abstract>
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%0 Conference Proceedings
%T A Morphology-Based Investigation of Positional Encodings
%A Ghosh, Poulami
%A Vashishth, Shikhar
%A Dabre, Raj
%A Bhattacharyya, Pushpak
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ghosh-etal-2024-morphology
%X Contemporary deep learning models effectively handle languages with diverse morphology despite not being directly integrated into them. Morphology and word order are closely linked, with the latter incorporated into transformer-based models through positional encodings. This prompts a fundamental inquiry: Is there a correlation between the morphological complexity of a language and the utilization of positional encoding in pre-trained language models? In pursuit of an answer, we present the first study addressing this question, encompassing 22 languages and 5 downstream tasks. Our findings reveal that the importance of positional encoding diminishes with increasing morphological complexity in languages. Our study motivates the need for a deeper understanding of positional encoding, augmenting them to better reflect the different languages under consideration.
%U https://aclanthology.org/2024.emnlp-main.1170
%P 21035-21045
Markdown (Informal)
[A Morphology-Based Investigation of Positional Encodings](https://aclanthology.org/2024.emnlp-main.1170) (Ghosh et al., EMNLP 2024)
ACL
- Poulami Ghosh, Shikhar Vashishth, Raj Dabre, and Pushpak Bhattacharyya. 2024. A Morphology-Based Investigation of Positional Encodings. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21035–21045, Miami, Florida, USA. Association for Computational Linguistics.