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20192022
most citedFew-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning

1 citations · 3 across the 7 of their papers we have counts for

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cs.CL20221 cited

Lexical Complexity Controlled Sentence Generation

Jinran Nie, Liner Yang, Yun Chen +3

Text generation rarely considers the control of lexical complexity, which limits its more comprehensive practical application. We introduce a novel task of lexical complexity contr…

cs.CL2022

COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation

Jiaxin Yuan, Cunliang Kong, Chenhui Xie +2

The definition generation task aims to generate a word's definition within a specific context automatically. However, owing to the lack of datasets for different complexities, the…

cs.CL2022

LitMind Dictionary: An Open-Source Online Dictionary

Cunliang Kong, Xuezhi Fang, Liner Yang +2

Dictionaries can help language learners to learn vocabulary by providing definitions of words. Since traditional dictionaries present word senses as discrete items in predefined in…

cs.CL2022

BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling

Cunliang Kong, Yujie Wang, Ruining Chong +4

This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2…

cs.CL2022

Multitasking Framework for Unsupervised Simple Definition Generation

Cunliang Kong, Yun Chen, Hengyuan Zhang +2

The definition generation task can help language learners by providing explanations for unfamiliar words. This task has attracted much attention in recent years. We propose a novel…

cs.CL2021

Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction

Zhenghao Liu, Xiaoyuan Yi, Maosong Sun +2

Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious cor…