融合术语和依存位置编码的英中专利复杂长句机器翻译

doi:10.11896/jsjkx.240600098

Abstract

Abstract: Existing neural machine translation methods still face some challenges when processing long complexsentences in patent texts.This paper first quantitatively defines long complex sentences in patent texts,and proposes a neural machine translation model that incorporates terminology information and dependency position encoding to address the problems of terminology omission and mistranslation,and sentence structure mistranslation in the translation process.The model integrates the constrained term vectorization into the attention module of the encoder and decoder and the output layer,and fuses dependency position encoding at the position encoding to alleviate the long-distance dependency problem.Experiments show that the proposed model significantly improves the term translation success rate and the overall translation performance of longcomplex sentences compared with several other baseline models.

Key words: Neural machine translation, Long complex sentences, Terms, Patent texts, Dependency position encoding

CLC Number:

TP391

LI Yonghui, YE Na, BAI Yu, ZHANG Guiping. Machine Translation of English-Chinese Long Complex Sentences in Patent Integrating Terminology and Dependency Position Encoding[J].Computer Science, 2025, 52(6A): 240600098-9.

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Machine Translation of English-Chinese Long Complex Sentences in Patent Integrating Terminology and Dependency Position Encoding

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