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

arXiv: https://arxiv.org/abs/2608.25343 · HTML: https://mlsanity.com/paper/2608.25343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://mlsanity.com/paper/2004.14166/) (2020)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://mlsanity.com/paper/2305.17721/) (2023)
- [Chinese Spelling Correction as Rephrasing Language Model](https://mlsanity.com/paper/2308.08796/) (2023)
- [CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers](https://mlsanity.com/paper/2211.08788/) (2022)
- [uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers](https://mlsanity.com/paper/2209.07068/) (2022)
- [A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models](https://mlsanity.com/paper/2410.04027/) (2024)
- [Trigger3: Refining Query Correction via Adaptive Model Selector](https://mlsanity.com/paper/2412.12701/) (2024)

## Abstract (arXiv)

Chinese query correction (CQC) is important for search and query recommendation on content platforms, but supervised methods rely on large annotated correction pairs that are costly to maintain as query vocabularies evolve. Unsupervised correction with language models is attractive, yet in the short-query setting, unconstrained generation often over-corrects ambiguous inputs toward high-frequency phrases, causing intent drift. We propose textscGUIDE, a generative unsupervised framework for CQC based on a confuse-then-clarify paradigm. textscGUIDE encodes phonetically or visually confusable characters with shared-IDs and reconstructs the original query with an encoder--decoder architecture, which constrains correction to plausible confusion neighborhoods while learning from unlabeled query streams. A time-decayed, query-frequency-weighted objective further supports adaptation to rapidly changing query vocabularies. Experiments on textitQSpell 250K and a large-scale real-world dataset (textitKwaiSearch) show that textscGUIDE consistently outperforms strong baselines, while online A/B testing further confirms gains in correction quality and downstream engagement.