A Study on English Translation Syntactic Reconstruction and Diversity Generation Driven by Diffusion Models
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Abstract
The lack of syntactic diversity and expressive variety in English translation hinders the improvement of translation quality. In response to this, a syntactic reconstruction and diversity generation method of English translation based on diffusion models is proposed in this paper. It consists of two parts: Firstly, it builds a syntax structure encoder from the source language sentences to embed them in a latent semantic space; Secondly, it designs a forward diffusion process to add Gaussian noise to gradually destroy the syntactic features of the source language; and then, it constructs a reverse denoising network in UNet architecture and self-attention mechanism and gradually recovers the target language syntax structure from the pure noise, and finally, it introduces conditional guidance mechanism into the reverse denoising network, which integrates semantic constraints and syntactic templates to control the style and structure of the generated sentences. Experiments on the WMT translation dataset show that the syntactic diversity index of the generated translation reaches 0.87, an improvement of 31.8 % compared to the Transformer baseline model; the BLEU score is 41.3, and the semantic fidelity score is 4.2. This method effectively solves the problem of sentence rigidity in translation systems and provides a new technical path for neural machine translation.
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