17 citations · 39 across the 11 of their papers we have counts for
11 papers
Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters
Yinghui Li, Zishan Xu, Shaoshen Chen +7
Writing assistance is an application closely related to human life and is also a fundamental Natural Language Processing (NLP) research field. Its aim is to improve the correctness…
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Lizhao Liu, Kunyang Lin, Shangxin Huang +4
Stroke is the basic element of Chinese character and stroke extraction has been an important and long-standing endeavor. Existing stroke extraction methods are often handcrafted an…
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…
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…
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Zhongli Li, Wenxuan Zhang, Chao Yan +4
Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives. Recent work shows that existing models memorize procedures from c…
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…
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
Zhongli Li, Wenhui Wang, Li Dong +2
Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to e…