Yuan Qi


SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Xingyi Cheng | Weidi Xu | Kunlong Chen | Shaohua Jiang | Feng Wang | Taifeng Wang | Wei Chu | Yuan Qi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds a graph over the characters, and SpellGCN is learned to map this graph into a set of inter-dependent character classifiers. These classifiers are applied to the representations extracted by another network, such as BERT, enabling the whole network to be end-to-end trainable. Experiments are conducted on three human-annotated datasets. Our method achieves superior performance against previous models by a large margin.

Question Directed Graph Attention Network for Numerical Reasoning over Text
Kunlong Chen | Weidi Xu | Xingyi Cheng | Zou Xiaochuan | Yuyu Zhang | Le Song | Taifeng Wang | Yuan Qi | Wei Chu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.