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Found 4 papers, 1 papers with code
面向汉语作为第二语言学习的个性化语法纠错(Personalizing Grammatical Error Correction for Chinese as a Second Language)
语法纠错任务旨在通过自然语言处理技术自动检测并纠正文本中的语序、拼写等语法错误。当前许多针对汉语的语法纠错方法已取得较好的效果, 但往往忽略了学习者的个性化特征, 如二语等级、母语背景等。因此, 本文面向汉语作为第二语言的学习者, 提出个性化语法纠错, 对不同特征的学习者所犯的错误分别进行纠正, 并构建了不同领域汉语学习者的数据集进行实验。实验结果表明, 将语法纠错模型适应到学习者的各个领域后, 性能得到明显提升。
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain.
Ranked #1 on
Machine Reading Comprehension
on BIOMRC
Improving Interpretability of Word Embeddings by Generating Definition and Usage
Definition modeling provides a more intuitive way to evaluate embeddings by utilizing them to generate natural language definitions of corresponding words.
Controllable Data Synthesis Method for Grammatical Error Correction
We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data.
