A French-to-English Machine Translation Model Using Transformer Network

Tian, Taoling; Chai, Shuhong; Jin, Ting; Huang, Hongyang

doi:10.1016/j.procs.2022.01.182

Cited by 25 publications

(13 citation statements)

References 5 publications

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“…Machine translation is combined with image and speech technology to help us solve the language barriers we encounter in our lives. Although the accuracy of machine translation is not enough to replace human translation, machine translation can still play a very good auxiliary role in real life [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised English Intelligent Machine Translation in Wireless Network Environment

Zhang

2022

Security and Communication Networks

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Researchers suggest unsupervised English machine translation to address the absence of parallel corpus in English translation. Unsupervised pretraining techniques, denoising autoencoders, back translation, and shared latent representation mechanisms are used to simulate the translation task using just monolingual corpora. This paper uses pseudo-parallel data to construct unsupervised neural machine translation (NMT) and dissimilar language pair analysis. This paper firstly analyzes the low performance of unsupervised translation on dissimilar language pairs from three aspects: bilingual word embedding quality, shared words, and word order. And artificial shared word replacement and preordering strategies are proposed to increase the shared words between dissimilar language pairs and reduce the difference in their syntactic structure, thereby improving the translation performance on dissimilar language pairs. The denoising autoencoder and shared latent representation mechanism in unsupervised English machine translation are only required in the early stage of training, and learning the shared latent representation limits the further improvement of performance in different directions. While training the denoising autoencoder by repeatedly altering the training data slows down the convergence of the model, this is especially true for divergent languages. This paper presents an unsupervised NMT model based on pseudo-parallel data to address this issue. It trains two standard supervised neural machine translation models using the pseudo-parallel corpus generated by the unsupervised neural machine translation system, which enhances translation performance and speeds convergence. Finally, the English intelligent translation model is deployed in the wireless network server, and users can access it through the wireless network.

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Section: Introductionmentioning

confidence: 99%

Unsupervised English Intelligent Machine Translation in Wireless Network Environment

Zhang

2022

Security and Communication Networks

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“…It is worth noting that this model can not only analyze words but also study English paragraphs and grammar. Aiming at the problems of long time and slow efficiency in French translation, RNN neural network algorithm is adopted to revise the traditional machine translation model [2]. us, an optimized machine translation model can be obtained, which can carry out targeted analysis of French pronunciation, language, and grammar.…”

Section: Introductionmentioning

confidence: 99%

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

Wen

2022

Security and Communication Networks

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In order to further improve the application of machine translation model in Japanese translation, analytic analysis method is adopted to optimize the original machine translation model. The improved machine translation model is used to analyze and describe Japanese translation. Finally, the optimized machine translation model is used to analyze Japanese multicontext. The relevant indexes and parameters were extracted and verified, and finally the model was verified by relevant experiments. The results show that the vector variation graph with different parameters can be divided into slow decline stage, stable change stage, and fast decline stage according to the increase of iteration number and the influence of corresponding change trend. In addition, it can be seen from the value of PE curve that the influence of parameter pe is the least, while the influence of corresponding re parameter is the greatest. The multicontext index of Japanese has the greatest influence on Japanese fluency and the least influence on Japanese keywords, and the trend of influence is parabolic. The application curve of the optimized machine translation model to Japanese in multiple contexts shows that different parameters have different effects on Japanese, which should be represented by the positive parameter V. Finally, the accuracy of the model is verified by experimental data. The above research can provide support for the application of machine learning in different fields and also provide research ideas for the multicontext translation of Japanese.

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“…Transformers Transformer is a deep learning architecture proposed by Google in 2017. It has achieved great success in the field of NLP [44][45][46]. Due to the unique attention mechanism and the excellent performance in the field of NLP, researchers have great interest in its application in trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

Multi-granularity scenarios understanding network for trajectory prediction

Yang

et al. 2022

Complex Intell. Syst.

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Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

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A French-to-English Machine Translation Model Using Transformer Network

Cited by 25 publications

References 5 publications

Unsupervised English Intelligent Machine Translation in Wireless Network Environment

Unsupervised English Intelligent Machine Translation in Wireless Network Environment

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

Multi-granularity scenarios understanding network for trajectory prediction

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