Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation

• URL: http://arxiv.org/abs/2505.16800v1
• Date: Thu, 22 May 2025 15:40:09 GMT
• Title: Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation
• Authors: Changbing Yang, Garrett Nicolai,
• Abstract summary: We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal.<n>Our framework jointly predicts morphological segments and glosses from orthographic input.<n>We integrate synthetic training data generated by large language models (LLMs) using in-context learning.
• Score: 7.766518675734386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framework jointly predicts morphological segments and glosses from orthographic input, leveraging shared linguistic representations obtained through a common documentary process to enhance model generalization. To further address data scarcity, we integrate synthetic training data generated by large language models (LLMs) using in-context learning. Experimental results on the SIGMORPHON 2023 dataset show that our approach significantly improves word-level segmentation accuracy and morpheme-level F1-score across multiple low-resource languages.

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