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

arXiv: https://arxiv.org/abs/2603.13320 · HTML: https://mlsanity.com/paper/2603.13320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance](https://mlsanity.com/paper/1905.02851/) (2019)
- [MFAQ: a Multilingual FAQ Dataset](https://mlsanity.com/paper/2109.12870/) (2021)
- [COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval](https://mlsanity.com/paper/2010.12800/) (2020)
- [WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval](https://mlsanity.com/paper/2502.20936/) (2025)
- [A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts](https://mlsanity.com/paper/2502.16767/) (2025)
- [Domain-specific Question Answering with Hybrid Search](https://mlsanity.com/paper/2412.03736/) (2024)
- [NepaliGPT: A Generative Language Model for the Nepali Language](https://mlsanity.com/paper/2506.16399/) (2025)
- [MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval](https://mlsanity.com/paper/2302.11953/) (2023)
- [Retrieval and Generative Approaches for a Pregnancy Chatbot in Nepali with Stemmed and Non-Stemmed Data : A Comparative Study](https://mlsanity.com/paper/2311.06898/) (2023)

## Abstract (arXiv)

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.