3 citations · 10 across the 7 of their papers we have counts for
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Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
Fanyi Qu, Chenming Tang, Yunfang Wu
Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some stu…
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?
Chenming Tang, Xiuyu Wu, Yunfang Wu
Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. We hypothesize that model ensemble based on the perplexity (PPL) compu…
An Error-Guided Correction Model for Chinese Spelling Error Correction
Rui Sun, Xiuyu Wu, Yunfang Wu
Although existing neural network approaches have achieved great success on Chinese spelling correction, there is still room to improve. The model is required to avoid over-correcti…
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation
Zichen Wu, Xin Jia, Fanyi Qu +1
Today the pre-trained language models achieve great success for question generation (QG) task and significantly outperform traditional sequence-to-sequence approaches. However, the…
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data
Fanyi Qu, Xin Jia, Yunfang Wu
Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficul…
Enhancing Question Generation with Commonsense Knowledge
Xin Jia, Hao Wang, Dawei Yin +1
Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context. Previous sequence-to-sequence models suffer…