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- Title
基于混合注意力Seq2seq模型的选项多标签分类.
- Authors
陈千; 韩林; 王素格; 郭鑫
- Abstract
The multi-label classification of options is an important part of the task of multiple-choice questions for reading comprehension of literature in college entrance examination (RCL-CEE). It can effectively improve the accuracy of multiple-choice questions by invoking different answering engines for different types of options. Option classification is regarded as a multi-label learning task since an option may have multiple characteristics for the complexity and variety of options. Traditional multi-label classification only considers the correlation between text and label, ignores the correlation between labels, and there exists strong semantic relevance within one option, which has great impact on label prediction. In order to handle these challenges, a hybrid attention based Seq2seq model is proposed, which considers the correlations from the option to the label and internal correlation of an option. Bi-LSTM is used to obtain the mutual information from the option to the label, and the multi-head self-attention is used to obtain the correlations semantics within one option. The label embedding is used to implicitly fuse semantic correlation between labels. Experimental results on the dataset of multiple-choice questions for RCL-CEE show that modeling above correlations can effectively improve the accuracy of options multi-label classification.
- Subjects
COLLEGE entrance examinations; READING comprehension; CLASSIFICATION
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 4, p104
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2108-0352