Method and device for extracting English-Myanmar bilingual parallel sentence pairs based on bilstm-cnn

Abstract

The invention relates to an English-Myanmar bilingual parallel sentence pair extraction method and device based on BiLSTM-CNN, and belongs to the technical field of natural language processing. The present invention first pre-trains bilingual word vectors through the Muse tool, then utilizes Burmese function words and auxiliary words to identify the characteristics of the subject-predicate-object of Burmese to carry out functional marking on the sentence, splicing the syntactic structure information of each word into the word vector, and then Use BiLSTM-CNN to encode the sentence, and use the output probability as a condition to measure whether it is a parallel sentence pair. And according to the above-mentioned steps, a bilingual parallel sentence pair extraction device based on BiLSTM-CNN is made. Compared with the traditional bilingual parallel sentence pair recognition system, the present invention is simpler. Experimental results show that the method and device are superior to the baseline system in terms of accuracy rate and recall rate and other indicators, and the accuracy rate is generally improved.

Description

technical field [0001] The invention relates to a BiLSTM-CNN-based English-Myanmar bilingual parallel sentence pair extraction method and device, and belongs to the technical field of natural language processing. Background technique [0002] In the field of natural language processing, the scale of parallel corpora plays an important role in improving the performance of machine translation. For Burmese, which is resource-scarce, the English-Myanmar parallel corpus resources are seriously scarce, and the quality of machine translation has not yet reached a practical level. The traditional methods of obtaining parallel corpora include manual translation and machine translation. The former is more costly and inefficient, while the latter relies on machine translation performance and poor quality. The scale of parallel corpus on the Internet is relatively small, while the comparable corpus is relatively large. How to use the large amount of English-Myanmar comparable corpus on ...

AI Technical Summary

Problems solved by technology

Traditional methods often only consider the semantic information of their own language. In fact, different languages ​​contain corresponding functional structures. Since the semantic expression of sentences is closely related to the syntactic structure, although the existing representation methods can retain the Word order information, but the loss of syntactic structure information cannot be avoided, and it is difficult to accurately learn sentence representation

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