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New Nepali passport QA dataset boosts retrieval performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new question-answering dataset specifically for Nepali passport-related services, addressing the scarcity of resources for low-resource languages. The dataset was used to fine-tune transformer-based embedding models, including SBERT and multilingual E5, for improved information retrieval. Evaluation showed that the fine-tuned SBERT models surpassed the baseline BM25, with multilingual E5 models achieving the highest retrieval performance. AI

IMPACT This research contributes to improving information access in low-resource languages, potentially enabling better public service applications.

RANK_REASON Academic paper detailing a new dataset and model evaluation for information retrieval in a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Nepali passport QA dataset boosts retrieval performance

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Academic paper detailing a new dataset and model evaluation for information retrieval in a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Funghang Limbu Begha, Praveen Acharya, Bal Krishna Bal ·

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

    arXiv:2603.13320v2 Announce Type: replace-cross 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 atte…

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