AIMC Topic: Translations

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Research on the Analysis of Correlation Factors of English Translation Ability Improvement Based on Deep Neural Network.

Computational intelligence and neuroscience
This paper adopts the algorithm of the deep neural network to conduct in-depth research and analysis on the factors associated with the improvement of English translation ability. This study focuses on text complexity, adding discourse complexity fea...

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment.

Computational intelligence and neuroscience
With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been g...

Unreferenced English articles' translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning.

PloS one
Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable...

Adoption of Wireless Network and Artificial Intelligence Algorithm in Chinese-English Tense Translation.

Computational intelligence and neuroscience
In order to solve the problem of tense consistency in Chinese-English neural machine translation (NMT) system, a Chinese verb tense annotation model is proposed. Firstly, a neural network is used to build a Chinese tense annotation model. During the ...

Enhancing Text Generation via Parse Tree Embedding.

Computational intelligence and neuroscience
Natural language generation (NLG) is a core component of machine translation, dialogue systems, speech recognition, summarization, and so forth. The existing text generation methods tend to be based on recurrent neural language models (NLMs), which g...

Heavyweight Statistical Alignment to Guide Neural Translation.

Computational intelligence and neuroscience
Transformer neural models with multihead attentions outperform all existing translation models. Nevertheless, some features of traditional statistical models, such as prior alignment between source and target words, prove useful in training the state...

A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU.

Computational intelligence and neuroscience
In the field of natural language processing (NLP), machine translation algorithm based on Transformer is challenging to deploy on hardware due to a large number of parameters and low parametric sparsity of the network weights. Meanwhile, the accuracy...

Research on Feature Extraction and Chinese Translation Method of Internet-of-Things English Terminology.

Computational intelligence and neuroscience
Feature extraction and Chinese translation of Internet-of-Things English terms are the basis of many natural language processing. Its main purpose is to extract rich semantic information from unstructured texts to allow computers to further calculate...

Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence.

Computational intelligence and neuroscience
To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignm...

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm.

Computational intelligence and neuroscience
Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technolo...