Xiaotian Zhang


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Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next?
Xiaotian Zhang | Yanjun Zheng | Hang Yan | Xipeng Qiu
Findings of the Association for Computational Linguistics: ACL 2023

While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous research has explored using information such as glyphs and phonetics to improve the ability of CSC models to distinguish misspelled characters, with good results at the accuracy level on public datasets. However, the generalization ability of these CSC models has not been well understood: it is unclear whether they incorporate glyph-phonetic information and, if so, whether this information is fully utilized. In this paper, we aim to better understand the role of glyph-phonetic information in the CSC task and suggest directions for improvement. Additionally, we propose a new, more challenging, and practical setting for testing the generalizability of CSC models. All code is made publicly available.

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Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Shimin Li | Xiaotian Zhang | Yanjun Zheng | Linyang Li | Xipeng Qiu
Findings of the Association for Computational Linguistics: ACL 2023

Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency in low-resource scenarios can be enhanced by mining alignment information uncertain utterance and deterministic dialogue state. Therefore, we innovatively implement dual learning in task-oriented dialogues to exploit the correlation of heterogeneous data. In addition, the one-to-one duality is converted into a multijugate duality to reduce the influence of spurious correlations in dual training for generalization. Without introducing additional parameters, our method could be implemented in arbitrary networks. Extensive empirical analyses demonstrate that our proposed method improves the effectiveness of end-to-end task-oriented dialogue systems under multiple benchmarks and obtains state-of-the-art results in low-resource scenarios.


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Chinese Coreference Resolution via Ordered Filtering
Xiaotian Zhang | Chunyang Wu | Hai Zhao
Joint Conference on EMNLP and CoNLL - Shared Task

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A Machine Learning Approach to Convert CCGbank to Penn Treebank
Xiaotian Zhang | Hai Zhao | Cong Hui
Proceedings of COLING 2012: Demonstration Papers


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Dependency Parser for Chinese Constituent Parsing
Xuezhe Ma | Xiaotian Zhang | Hai Zhao | Bao-Liang Lu
CIPS-SIGHAN Joint Conference on Chinese Language Processing