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Found 11 papers, 4 papers with code
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning
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.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences.
Mining Error Templates for Grammatical Error Correction
We have accumulated 1, 119 error templates for Chinese GEC based on this method.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
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.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
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.
Ranked #4 on
Entity Linking
on FUNSD
Chunk-based Chinese Spelling Check with Global Optimization
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
Optical Character Recognition (OCR)
+1
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-byword matching.
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
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.
Ranked #1 on
Natural Language Inference
on QNLI
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction
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.
Neural Regularized Domain Adaptation for Chinese Word Segmentation
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.
N-gram Model for Chinese Grammatical Error Diagnosis
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.
