Li Huang


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PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Li Huang | Junjie Li | Weiwei Jiang | Zhiyu Zhang | Minchuan Chen | Shaojun Wang | Jing Xiao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rely on external resources to model their similarities. To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information. Specifically, we derive pinyin and glyph representations for Chinese characters from audio and visual modalities respectively, which are integrated into a pre-trained language model by a well-designed adaptive gating mechanism. To verify its effectiveness, we conduct comprehensive experiments and ablation tests. Experimental results on three shared benchmarks demonstrate that our model consistently outperforms previous state-of-the-art models.

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GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction
Junyi Bian | Li Huang | Xiaodi Huang | Hong Zhou | Shanfeng Zhu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
Rubungo Andre Niyongabo | Qu Hong | Julia Kreutzer | Li Huang
Proceedings of the 28th International Conference on Computational Linguistics

Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, we introduce two news datasets (KINNEWS and IRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible, but while Kinyarwanda has been studied in Natural Language Processing (NLP) to some extent, this work constitutes the first study on Kirundi. Along with the datasets, we provide statistics, guidelines for preprocessing, and monolingual and cross-lingual baseline models. Our experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. In addition, the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER. The datasets, stopwords, and pre-trained embeddings are publicly available at


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Ch2R: A Chinese Chatter Robot for Online Shopping Guide
Peijie Huang | Xianmao Lin | Zeqi Lian | De Yang | Xiaoling Tang | Li Huang | Qiang Huang | Xiupeng Wu | Guisheng Wu | Xinrui Zhang
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing