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arXiv Computation and Language By Lei Yang, Binbin Huang, Jiwei Tan, Xuhui Sui, Chang Tu, Yi Wang, Han Li

GUIDE: Generative Unsupervised Chinese Query Correction via Phonetic and Visual Shared-ID Encoding

Read the original on arXiv Computation and Language →

The paper introduces GUIDE, a generative unsupervised framework for Chinese query correction that uses shared-ID encoding for phonetically or visually confusable characters and an encoder–decoder architecture to reconstruct queries within plausible confusion neighborhoods. It incorporates a time‑decayed, query‑frequency‑weighted objective to adapt to rapidly changing query vocabularies. Experiments on QSpell 250K and the large‑scale KwaiSearch dataset demonstrate that GUIDE consistently outperforms strong baselines, with online A/B testing confirming improvements in correction quality and downstream engagement.

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 Computation and Language.

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