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20202022
most citedPrototypical Representation Learning for Relation Extraction

38 citations · 43 across the 7 of their papers we have counts for

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11 papers · 1 filter

cs.CL2023

Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction

Yinghui Li, Shirong Ma, Shaoshen Chen +5

Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human daily life and various downstream tasks. Recent approaches mainly emp…

cs.CL2023

Counterfactual Debiasing for Generating Factually Consistent Text Summaries

Chenhe Dong, Yuexiang Xie, Yaliang Li +1

Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important y…

cs.CL2023

CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction

Jingheng Ye, Yinghui Li, Qingyu Zhou +4

Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possi…

cs.CL20221 cited

Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation

Shulin Huang, Shirong Ma, Yinghui Li +4

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG res…

cs.CL20221 cited

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

Shirong Ma, Yinghui Li, Rongyi Sun +9

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the…

cs.CL2021

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression

Chenhe Dong, Yaliang Li, Ying Shen +1

On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Neverthel…