Model and Verification of Medical English Machine Translation Based on Optimized Generalized Likelihood Ratio Algorithm

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase’s part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation.

English Machine Translation Model Based on an Improved Self-Attention Technology

English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.

The Construction of Machine Translation Model and Its Application in English Grammar Error Detection

In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is constructed through the steps of word vector generation, encoder language model construction, decoder language model construction, word alignment, output module, and so on. On this basis, the machine translation model is trained to detect English grammatical errors through dependency analysis and alternative word generation. Experimental results show that the accuracy, recall rate, and F1 value of the proposed method are higher than those of the experimental comparison method for detecting English grammatical errors such as articles, prepositions, nouns, verbs, and subject-verb agreement, indicating that the proposed method is of high practical value.

Revisiting translation in the age of digital globalization

Globalization has gone digital and presents a new type of connectivity virtually today. Digital globalization has transformed the landscape of translation theory and practice, exerting considerable influence on translation studies and the profession of translators. The translation practice evolves with the change of literary expectations driven by the digital revolution. New translation modes have been cultivated by incorporating two essential features of the age, known as technology and participation. Against this backdrop, Chinese web fiction is going global with establishing and developing overseas volunteer translation websites. With this in mind, this paper analyzes the translation model of Chinese web fiction with respect to digital globalization and argues that the fan-based volunteer translation has emerged as a new paradigm that features the “user participation turn” in translation studies.

A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning

Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high-level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning-based end-to-end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource-poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end-to-end neural network machine translation models under low-resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.

PhraseAttn: Dynamic Slot Capsule Networks for phrase representation in Neural Machine Translation

Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.

Dual contextual module for neural machine translation

Self-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending to all tokens in the sentence. Despite improvements in performance, recent research argues that the self-attention mechanism tends to concentrate more on the global context with less emphasis on the contextual information available within the local neighbourhood of tokens. This work presents the Dual Contextual (DC) module, an extension of the conventional self-attention unit, to effectively leverage both the local and global contextual information. The goal is to further improve the sentence representation ability of the encoder and decoder subnetworks, thus enhancing the overall performance of the translation model. Experimental results on WMT’14 English-German (En$$\rightarrow $$ → De) and eight IWSLT translation tasks show that the DC module can further improve the translation performance of the Transformer model.

Piloting Strategies in Translating Adjective

<p><strong>Parts of speech are important knowledge that needs to be known in the learning language unit to find out the appropriate meaning. Parts of speech indicate the function of the word in forming the meaning within the sentence. This research investigates the translation model of emotional intelligence terms in root word and derivative, especially in adjective type as piloting strategies in translating adjective. The research objectives are (i) to construct a translation model of translating adjectives, (ii) to describe the adjective translation model, and (iii) to describe the way to use the model. The study was using a research development approach. A model was a product of the translation stage based on translation techniques in producing good translation quality. The research was developed based on the research conducted by Nur Hasyim (2019) about “The Translation Analysis of Emotional Intelligence Terms on the book entitled <em>Working with Emotional Intelligence</em> by Daniel Goleman (2019)”. The substance of the model was based on proposed translation techniques such as <em>established-</em> <em>equivalent, transposition, modulation, or borrowing</em>. Those techniques are the considerable techniques that can be used to navigate in translating adjectives to obtain good translation quality</strong>.</p><p><strong><span>Keywords - </span></strong><em><span>Adjective,</span></em><span> <em>Emotional intelligence, Developmental research, Piloting strategies, Translation model, Translation quality.</em></span></p>