Contextual Spelling Correction with Language Model for Low-resource Setting

• URL: http://arxiv.org/abs/2404.18072v1
• Date: Sun, 28 Apr 2024 05:29:35 GMT
• Title: Contextual Spelling Correction with Language Model for Low-resource Setting
• Authors: Nishant Luitel, Nirajan Bekoju, Anand Kumar Sah, Subarna Shakya,
• Abstract summary: A small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Probability of error happening(error model) is extracted from the corpus. Combination of LM and error model is used to develop the SC model through the well-known noisy channel framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM and error model is used to develop the SC model through the well-known noisy channel framework. The effectiveness of this approach is demonstrated through experiments on the Nepali language where there is access to just an unprocessed corpus of textual data.

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