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arXiv Machine Learning By Funghang Limbu Begha, Praveen Acharya, Bal Krishna Bal

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

Read the original on arXiv Machine Learning →

The paper introduces a Nepali Question‑Answer dataset focused on passport‑related FAQs to support information retrieval in a low‑resource language. The authors fine‑tune transformer‑based embedding models for semantic similarity and compare them against the BM25 baseline. Their experiments show that fine‑tuned SBERT models outperform BM25, while multilingual E5 embeddings achieve the best overall retrieval performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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