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This study explores the POS tagging for four Hindi dialects Awadhi, Bhojpuri, Maithili, and Braj using Few-Shot learning models. The research focuses on identification of four building blocks such as Noun, Pronoun, Verb and Auxiliary Verb (Aux). The authors have specifically identified this work because no prior research has been done for these Hindi dialects using Few-Shot learning. Despite working with a limited amount of annotated data, the model presented good result with Awadhi dialect among the other dialects. The evaluation was conducted using four metrics such as Precision, Recall, F1 Score, and Accuracy, all of which were visually represented through histograms. These results show that few-shot learning can be very useful for working with languages that don’t have much data. It also gives a strong starting point for more progress in this field.

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POS Tagging Using Few-Shot Learning for Resource-Scarce Hindi Dialects

  • 会议论文

  • 发表日期：

    2026年

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  • Hema Gaikwad,
  • Jatinderkumar R. Saini

摘要

This study explores the POS tagging for four Hindi dialects Awadhi, Bhojpuri, Maithili, and Braj using Few-Shot learning models. The research focuses on identification of four building blocks such as Noun, Pronoun, Verb and Auxiliary Verb (Aux). The authors have specifically identified this work because no prior research has been done for these Hindi dialects using Few-Shot learning. Despite working with a limited amount of annotated data, the model presented good result with Awadhi dialect among the other dialects. The evaluation was conducted using four metrics such as Precision, Recall, F1 Score, and Accuracy, all of which were visually represented through histograms. These results show that few-shot learning can be very useful for working with languages that don’t have much data. It also gives a strong starting point for more progress in this field.