Using Contextual Information for Sentence-level Morpheme Segmentation

Fuente: arXiv
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Auteurs principaux: Bhandari, Prabin, Paudel, Abhishek
Format: Preprint
Publié: 2024
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author Bhandari, Prabin
Paudel, Abhishek
author_facet Bhandari, Prabin
Paudel, Abhishek
contents Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within the sentence. In this study, we redefine the morpheme segmentation task as a sequence-to-sequence problem, treating the entire sentence as input rather than isolating individual words. Our findings reveal that the multilingual model consistently exhibits superior performance compared to monolingual counterparts. While our model did not surpass the performance of the current state-of-the-art, it demonstrated comparable efficacy with high-resource languages while revealing limitations in low-resource language scenarios.
format Preprint
id arxiv_https___arxiv_org_abs_2403_15436
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Using Contextual Information for Sentence-level Morpheme Segmentation
Bhandari, Prabin
Paudel, Abhishek
Computation and Language
Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within the sentence. In this study, we redefine the morpheme segmentation task as a sequence-to-sequence problem, treating the entire sentence as input rather than isolating individual words. Our findings reveal that the multilingual model consistently exhibits superior performance compared to monolingual counterparts. While our model did not surpass the performance of the current state-of-the-art, it demonstrated comparable efficacy with high-resource languages while revealing limitations in low-resource language scenarios.
title Using Contextual Information for Sentence-level Morpheme Segmentation
topic Computation and Language
url https://arxiv.org/abs/2403.15436