A Morphology-Based Investigation of Positional Encodings

• URL: http://arxiv.org/abs/2404.04530v2
• Date: Thu, 30 May 2024 14:44:10 GMT
• Title: A Morphology-Based Investigation of Positional Encodings
• Authors: Poulami Ghosh, Shikhar Vashishth, Raj Dabre, Pushpak Bhattacharyya,
• Abstract summary: 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.
• Score: 46.667985003225496
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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