Papers by Houquan Zhou
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)
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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
| Challenge: | Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph. |
| Approach: | They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase. |
| Outcome: | The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets. |
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)
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| Challenge: | Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes. |
| Approach: | They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task. |
| Outcome: | The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models. |
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)
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| Challenge: | Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance. |
| Approach: | They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder. |
| Outcome: | The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0. |
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)
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| Challenge: | Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit. |
| Approach: | They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets. |
A Training-free LLM-based Approach to General Chinese Character Error Correction (2025.acl-long)
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| Challenge: | Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in text. |
| Approach: | They propose a task that handles missing and redundant characters and an additional prompt-based large language model to improve performance. |
| Outcome: | The proposed task is based on a high-quality dataset and a prompt-based large language model. |
Mixture of Small and Large Models for Chinese Spelling Check (2025.acl-long)
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| Challenge: | Chinese Spelling Check (CSC) tasks have been developed to correct spelling errors in given sentences . fine-tuned BERT-based models show excellent performance but suffer from edit pattern overfitting . a novel mixture approach that effectively combines small models and LLMs during beam search decoding phase improves accuracy and fluency of LLM. |
| Approach: | They propose a dynamic mixture approach that effectively combines small models and LLMs during beam search decoding phase. |
| Outcome: | The proposed method significantly boosts error correction capabilities, achieving state-of-the-art results across multiple datasets. |