Baolong LiThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Foreign Languages, Zhengzhou University of Science and Technology Zhengzhou 450064 China
Received: October 8, 2024 Accepted: November 1, 2024 Publication Date: January 13, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Bilingual parallel corpora is a very important basic resource in the research field of natural language processing based on statistics. There are cross alignment and empty alignment in Chinese-English bilingual text, it is easy to affect the effect of Chinese-English sentence alignment. Therefore, we propose a novel Chinese-English sentence alignment method based on multi-feature self-attention mechanism fusion. First, the long features of Chinese-English bilingual sentences are integrated into the Glove word vector. Then bidirectional gated recurrent unit is used to encode the feature word vector to obtain more fine-grained sentence local information. Second, the interactive attention mechanism is introduced to extract global information in bilingual sentences to ensure the effective use of contextual semantic features. Finally, the Kuhn-Munkres (KM) algorithm is introduced on the basis of multi-layer perceptron, which can deal with non-monotonic aligned text and improve the generalization ability of the model. Experiments show that, the F index with the proposed method exceeds 90%, the proposed method can effectively improve the correct rate and recall rate of sentence alignment, and improve the construction efficiency of Chinese-English parallel corpora.
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