Baoxin Wang

Baoxin Wang

he/him

Researcher

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

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portfolio

publications

Dynamic Connected Networks for Chinese Spelling Check

Published in Findings of ACL 2021, 2021

Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text. Most state-of-the-art works on the CSC task adopt a BERT-based non-autoregressive language model, which relies on the output independence assumption. The inappropriate independence assumption prevents BERT-based models from learning the dependencies among target tokens, resulting in an incoherent problem. To address the above issue, we propose a novel architecture named Dynamic Connected Networks (DCN), which generates the candidate Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters. The experimental results show that our proposed method achieves a new state-of-the-art performance on three human-annotated datasets.

Recommended citation: Baoxin Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Guoping Hu, and Ting Liu. 2021. In Findings of the Association for Computational Linguistics (ACL 2021).
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CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers

Published in COLING 2022, 2022

The Chinese text correction (CTC) focuses on detecting and correcting Chinese spelling errors and grammatical errors. Most existing datasets of Chinese spelling check (CSC) and Chinese grammatical error correction (GEC) are focused on a single sentence written by Chinese-as-a-second-language (CSL) learners. We find that errors caused by native speakers differ significantly from those produced by non-native speakers. These differences make it inappropriate to use the existing test sets directly to evaluate text correction systems for native speakers. Some errors also require the cross-sentence information to be identified and corrected. In this paper, we propose a cross-sentence Chinese text correction dataset for native speakers. Concretely, we manually annotated 1,500 texts written by native speakers. The dataset consists of 30,811 sentences and more than 1,000,000 Chinese characters. It contains four types of errors: spelling errors, redundant words, missing words, and word ordering errors. We also test some state-of-the-art models on the dataset. The experimental results show that even the model with the best performance is 20 points lower than humans, which indicates that there is still much room for improvement. We hope that the new dataset can fill the gap in cross-sentence text correction for native Chinese speakers.

Recommended citation: Baoxin Wang, Xingyi Duan, Dayong Wu, Wanxiang Che, Zhigang Chen, and Guoping Hu. 2022.In Proceedings of the 29th International Conference on Computational Linguistics (COLING 2022).
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CINO: A Chinese Minority Pre-trained Language Model

Published in COLING 2022, 2022

Multilingual pre-trained language models have shown impressive performance on cross-lingual tasks. It greatly facilitates the applications of natural language processing on low-resource languages. However, there are still some languages that the current multilingual models do not perform well on. In this paper, we propose CINO (Chinese Minority Pre-trained Language Model), a multilingual pre-trained language model for Chinese minority languages. It covers Standard Chinese, Yue Chinese, and six other ethnic minority languages. To evaluate the cross-lingual ability of the multilingual model on ethnic minority languages, we collect documents from Wikipedia and news websites, and construct two text classification datasets, WCM (Wiki-Chinese-Minority) and CMNews (Chinese-Minority-News). We show that CINO notably outperforms the baselines on various classification tasks. The CINO model and the datasets are publicly available at http://cino.hfl-rc.com.

Recommended citation: Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, and Zhigang Chen. 2022. In Proceedings of the 29th International Conference on Computational Linguistics (COLING 2022).
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Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization

Published in ACL 2025, 2025

Recommended citation: Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, and Bing Qin. 2025. Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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