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arXiv Machine Learning By Anshul Garg, Pavni Tandon, Karan Bhukar, Tanmay Khandelwal, Ujjal Kumar Dutta

Low-Latency Spell Correction for Japanese Music Search Queries

Read the original on arXiv Machine Learning →

The paper introduces a compact BART-based sequence‑to‑sequence model for low‑latency spell correction of Japanese music search queries, addressing challenges posed by four writing scripts. It employs a script‑aware synthetic misspelling pipeline that blends keyboard‑layout models, phonetic confusion priors, consonant alternations, and kana case errors, and normalizes mixed‑script titles to a single canonical script to reduce hallucinations. Experiments show the model achieves 41.09% exact‑match accuracy and 11.62% CER, outperforming edit‑distance baselines while keeping inference latency under 4 ms on a single GPU.

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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