3 citations · 9 across the 6 of their papers we have counts for
6 papers · 1 filter
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Yinghui Li, Shirong Ma, Qingyu Zhou +7
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal inform…
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
Yusen Zhang, Zhongli Li, Qingyu Zhou +5
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers. The OCR model easily gets con…
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Shaopeng Lai, Qingyu Zhou, Jiali Zeng +4
Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performi…
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Yinghui Li, Qingyu Zhou, Yangning Li +7
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity. Recently, pre-trained language mo…
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Heng-Da Xu, Zhongli Li, Qingyu Zhou +5
Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language. Most of the Chinese spelling errors are misused semant…
Improving BERT with Syntax-aware Local Attention
Zhongli Li, Qingyu Zhou, Chao Li +2
Pre-trained Transformer-based neural language models, such as BERT, have achieved remarkable results on varieties of NLP tasks. Recent works have shown that attention-based models…