Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications

2026-03-04

arXiv.org

AI Summary

为解决尼泊尔语信息检索系统缺乏标注数据的问题，构建了护照服务相关的问答数据集，并使用微调的基于转换器的嵌入模型和混合检索方法提高检索性能。

Abstract

Nepali, a low-resource language, faces significant challenges in building an effective information retrieval system due to the unavailability of annotated data and computational linguistic resources. In this study, we attempt to address this gap by preparing a pair-structured Nepali Question-Answer dataset. We focus on Frequently Asked Questions (FAQs) for passport-related services, building a data set for training and evaluation of IR models. In our study, we have fine-tuned transformer-based embedding models for semantic similarity in question-answer retrieval. The fine-tuned models were compared with the baseline BM25. In addition, we implement a hybrid retrieval approach, integrating fine-tuned models with BM25, and evaluate the performance of the hybrid retrieval. Our results show that the fine-tuned SBERT-based models outperform BM25, whereas multilingual E5 embedding-based models achieve the highest retrieval performance among all evaluated models.

Problem

• Nepali
• low-resource language
• information retrieval
• annotated data
• FAQs

Innovation

• fine-tuned transformer-based embedding models
• hybrid retrieval approach
• low-resource language dataset