Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation

Abstract

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation.

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

“…Ni reduces the impact of unregistered words on the overall translation performance of sentences through data generalization, improves the translation quality of unregistered words themselves, and uses a multi-coverage fusion model to improve the attention scoring mechanism to further alleviate overtranslation and neural MT in neural MT (missing translation problem) [13]. Wang et al used semantic role information to label nonterminal symbols in syntactic translation models, making translation rules more discriminative, and incorporating semantic information as a feature into existing translation models [14]. Misra proposed a CNN model that combines word and sentence features for Chinese entity relation extraction and achieved better results than existing models on the ACE2005 dataset [15].…”

English Lexical Analysis System of Machine Translation Based on Simple Recurrent Neural Network

“…Neural network machine translation can alleviate the problems of statistical machine translation to a great extent and gradually show better translation performance. It has become the core technology of many commercial online machine translation systems [ 15 ]. It is generally composed of encoder and decoder structures, and their network structures can be different [ 16 ].…”