Search Results for author: Zuyi Bao

Found 11 papers, 4 papers with code

Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning

no code implementations AACL (NLP-TEA) 2020 Yikang Luo, Zuyi Bao, Chen Li, Rui Wang

For the correction subtask, we utilize the masked language model, the seq2seq model and the spelling check model to generate corrections based on the detection results.

Language Modelling Multi-Task Learning

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

1 code implementation NAACL 2022 Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.

Grammatical Error Correction Pretrained Language Models

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

no code implementations EMNLP 2021 Yue Zhang, Bo Zhang, Rui Wang, Junjie Cao, Chen Li, Zuyi Bao

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i. e., semantic entity), while the relations in-between are largely unexplored.

Dependency Parsing Entity Linking +2

Chunk-based Chinese Spelling Check with Global Optimization

no code implementations Findings of the Association for Computational Linguistics 2020 Zuyi Bao, Chen Li, Rui Wang

Chinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length.

Optical Character Recognition

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations

1 code implementation IJCNLP 2019 Zuyi Bao, Rui Huang, Chen Li, Kenny Q. Zhu

Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-byword matching.

Language Modelling NER +1

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

no code implementations ICLR 2020 Wei Wang, Bin Bi, Ming Yan, Chen Wu, Zuyi Bao, Jiangnan Xia, Liwei Peng, Luo Si

Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.

Language Modelling Linguistic Acceptability +5

A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

no code implementations WS 2018 Chen Li, Junpei Zhou, Zuyi Bao, Hengyou Liu, Guangwei Xu, Linlin Li

In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types.

Grammatical Error Correction TAG

Neural Regularized Domain Adaptation for Chinese Word Segmentation

no code implementations WS 2017 Zuyi Bao, Si Li, Weiran Xu, Sheng Gao

For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature.

Chinese Word Segmentation Domain Adaptation +2

N-gram Model for Chinese Grammatical Error Diagnosis

no code implementations WS 2017 Jianbo Zhao, Hao liu, Zuyi Bao, Xiaopeng Bai, Si Li, Zhiqing Lin

Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis. This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task.

Language Modelling

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