Computer-aided Translation Systems Research Articles

Enhancing computer-aided translation system with BiLSTM and convolutional neural network using a knowledge graph approach

Enhancing computer-aided translation system with BiLSTM and convolutional neural network using a knowledge graph approach

The impact of CS structured computer-assisted translation system on English linguistics teaching in universities

Abstract In the age of information technology, the teacher is no longer the only source of information, and a single lecture mode can trigger a range of messages for learning emotions. For this reason, this paper designs a cs-structured computer-aided translation system composed of machine translation in a neural network. After solving the exposure bias problem, the model regularization method combined with the bi-directional decoding consistency is used to optimize the assisted translation system. Then, aspects of the effectiveness of students’ use of the system on their English proficiency improvement were studied. The test scores of the test and comparison classes showed that the mean values of the scores before and after the test of the test class were 13.58 and 15.94, with a mean difference of 2.36 points. The mean values of the scores before and after the test of the comparison class were 14.58 and 14.94, respectively, with a mean difference of 0.4. Comparing the absolute values of the mean differences between the scores before and after the test of the two classes, it is clear that the test class improved their English proficiency level significantly more than the comparison class. Furthermore, the overall satisfaction of the teachers using the system reached 85.62%. Therefore, in terms of traditional teaching methods, the assisted translation system is more capable of improving students’ English proficiency. It enables teachers to improve English teaching efficiency in the classroom and promotes the modernization and intelligence of English teaching.

Development of semi-automatic multilingual terminological resources

The text suggests a methodology for semi-automatic creation of terminological resources for the Bulgarian language for computer-aided translation systems. For this purpose, the technical characteristics of computer-assisted translation systems and their components (translation memory, terminological base, machine translation) are described and analyzed. The results of a survey aimed at translators in Bulgaria and their opinion regarding this type of technology are described. The proposed methodology does not claim to be exhaustive, but takes into account the processes that translators follow in their work. Through the proposed methodology, an English-Bulgarian terminological base in the field of computer terminology was also created.

Design and Proofreading of the English-Chinese Computer-Aided Translation System by the Neural Network.

At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.

Retracted: Design and Proofreading of the English-Chinese Computer-Aided Translation System by the Neural Network.

[This retracts the article DOI: 10.1155/2023/9450816.].

Design of Computer-Aided Translation System Based on Naive Bayesian Algorithm.

With the progress of society and the rapid development of science and technology, computer translation technology has become an important auxiliary tool in the fields of software localization and technical translation. This realistic demand has prompted translators to pay more attention to computer translation and have made some useful explorations on this basis. This paper aims to study and discuss computer-aided translation systems based on the fusion of naive Bayesian algorithms. This paper theoretically analyzes some key technologies in computer-aided translation. Computer-aided translation refers to helping translators to translate texts with a series of tools and then proposes a Bayesian classification algorithm. Translation memory technology can solve many practical problems, especially in the machinery manufacturing industry, processing some sentences in documents, which can reduce repetitive labor, unify vocabulary, and make translation styles more coordinated. The experimental results of this paper show that applying the naive Bayes method to the computer-aided translation system can better classify the documents in the translation system, thereby improving the ability of computer-aided translation. When the proportion of professional terms in the article reaches 85%, computer-aided translation has an auxiliary role for the translator. When the proportion of professional terms in the article reaches about 95%, computer-assisted translation can efficiently speed up the work speed and quality of translators. Due to the prosperity of computer translation systems, the duplication of labor for translators has been significantly reduced, and this ensures the consistency of terminology and translation style, so that the fruits of labor are fully utilized.

Innovation and Exploration of Computer-Aided New Media Translation Course Teaching Mode under the Ecological Environment.

In order to analyze the innovation of computer new media-aided translation course teaching mode under the ecological environment, this paper introduces the CorpTrans system, which analyzes the practical effect of computer-aided translation system applied in the course teaching, verifies the role of the system in computer-aided translation course teaching, and plays an important role in improving students' translation ability. At the same time, starting with the development trend of new media environment, the CorpTrans system is used to innovate the teaching mode of computer-aided translation course. The teaching ecology of the comprehensive new media translation curriculum consists of the interaction between the ecological subject of teaching and the ecological environment of teaching. Through the efforts of both teachers and students, high-quality computer-aided translation courses will contribute to cultivating compound translation talents. The innovation of teaching mode needs to be realized by this system, so as to better meet the requirements of improving the teaching level of translation courses.

Design of computer aided translation system for English communication language based on grey clustering evaluation

In order to facilitate communication and communication, this paper studies the optimization of the current computer-aided translation system, and proposed a design method of English communication language computer-aided translation system based on grey clustering evaluation. By optimizing the hardware configuration and algorithm function keys of the system, the English translation mechanism of multilanguage interaction, the design idea of editing and modifying after English translation and knowledge database management are realized, and the system function framework was constructed, including the system transceiver unit, automatic translation unit, manual correction unit, task management unit and memory management unit, the performance of task management unit and memory management unit is analyzed. On this basis, the specific work flow of the design system mainly includes the English translation service flow integrating multilanguage interaction and the project-based multilanguage interaction English translation service flow design, which realizes the English translation online assistance under the multilanguage interaction environment. The experimental results show that the design system has the advantages of high online translation speed, pronunciation success rate and multilanguage translation success rate.

La constitución de una memoria terminológica: elaboración de terminologías bilingües en programas de memoria de traducción

Computer-aided translation systems (CAT) based on Translation Memories (TM) are a widely diffused technology that uses database and code-protection features to improve the quality, efficiency and consistency of the human translation process. These systems basically consist of a textual database in which each source sentence of a translation is stored together with the target sentence (this is called a translation memory “unit”). New and changed translation proposals will then be stored in the database for future use. This textual database – the kernel of the system – is combined with a terminological database (TDB), which is used by translators to store independently, terminological equivalences or translation units of particular value.In this paper the authors outline a first draft of a methodology that describes the preparation of a bilingual terminology from – and within – TM applications. The bilingual corpus produced is called the ‘terminological memory’ of the translator.

On The Auxiliary Role of Computer and Network in Japanese Translation

In recent years, with the rapid development of information technology, computer-aided translation system has gradually entered people’s vision. With its advantages of high efficiency and low cost, it has attracted much attention in many linguistic fields. With the development of the international era, people are no longer satisfied with artificial Japanese translation, they prefer to use computer-aided translation system. If we can’t know the culture of Japan, artificial translation can’t make people understand the real meaning of Japanese sentences. However, the computer with huge information storage can solve this problem. This paper includes systematic summary of the computer-aided system and the analysis of the affect of computer-aided in the process of Japanese translation.

Improving the Translation Environment for Professional Translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

Research on Alignment in the Construction of Parallel Corpus

Parallel corpora are of great value in the field of machine translation and cross-language information retrieval. Benefiting from the development of machine learning and deep learning, the technology of the construction of corpus evolves from vocabulary alignment, phrase alignment to chunk alignment. The high quality of automatic bilingual chunks alignment in corpus plays an important role in the performance improvement of machine translation systems, especially in computer-aided translation systems. In the study, the degree of adhesion and relaxation is used to measure the tightness and looseness of the inter-word connection when a chunk is identified, which can be expressed by a mathematical mode. The task of chunk alignment in the construction of a parallel corpus can be described as the three steps: input bilingual sentences, segment chunks, and semantic alignment. At present, most algorithms are based on statistical methods, and the output alignment results are machine-oriented.

A Chinese to Tibetan Machine Translation System with Multiple Translating Strategies

This paper proposes a Chinese to Tibetan machine translation system with multiple translating strategies. The key corpora and technologies are explained in detail. Experiments show the sub systems output the translation of each phrase in the same order as they are in the Chinese sentence rather than in a Tibetan sentence, which leads to worse translation quality. So an order adjusting model is essential to Chinese to Tibetan translation system. The recall of translation phrase makes an improvement of 9.71% over the popular off-the-shelf language neutral statistical machine translation programme Moses. Our translation system achieves a speed ofabout 0.175s per sentence, which meets the requirement of the computer aided translation system.

Facilitating cross-cultural understanding with learning activities supported by speech-to-text recognition and computer-aided translation

We designed and implemented cross-cultural learning activities for this study. Participants from two countries, geographically located very far from each other, represented different cultures that have no communication language in common. Two systems were applied to learning activities in order to enable interaction and information exchange among the participants: (1) a speech-to-text recognition system, which generates texts from a speaker's voice input in his/her native language and (2) computer-aided translation system, which simultaneously translates texts into the language of the speaker's foreign peers. The goal was to test the feasibility of learning activities supported by the two systems and their effectiveness for cross-cultural learning. To this end, we evaluated participants' learning outcomes, analyzed their online communication with peers, and carried out a questionnaire survey and interviews with both the participants and their instructors. The use of multiple data sources allowed triangulation of the findings, thus adding rigor to the research. We obtained three findings through this study. First, cross-cultural learning took place. Second, the questionnaire and interview results show that the two systems are easy to use and useful for cross-cultural learning. According to participants, even with no common language, they could still interact and exchange culture-related information using the two systems. Finally, the results indicate that the texts produced by the two systems are acceptable and useful for the cross-cultural learning of participants (except texts translated from Russian into Chinese in the second week). These findings suggest that the educators and researcher can implement cross-cultural learning activities for participants with no common language with the support of speech-to-text recognition and computer-aided translation systems, as these two systems can help participants to communicate and exchange culture-related information.

Artículo Necesidad de políticas de información y de sus profesionales para la automatización de la producción de documentación técnica en el entorno de la industria GILT

The production of technical documentation has increased considerably over the last few years, as reflected by the development of the GILT (globalisation, internationalisation, localisation, translation) industry. The production cycle for multilingual technical documentation is an industrial process undergoing several different phases. One of these phases is translation, which in professional environments systematically uses computer-aided translation systems. These systems manage linguistic resources such as translation memories and terminology databases, which are exploited by extracting, retrieving and reusing data. These linguistic resources together with technical documents are the most important linguistic assets a translation company has. Therefore, an information policy based on documentary strategies is necessary to prevent the under-utilisation of such assets and thus to improve their exploitation.

Closed Captioning in Real Time

The line-21 captioning system was developed in the 1970s as a means of delivering hidden subtitles (closed captions) to hearing-impaired television viewers. A more recent development is the ability to generate captions in “real time” during live broadcasts, using experienced court reporters and a computer-aided translation system that converts shorthand code into captions almost instantaneously. More than 70 hours of nationally broadcast programming are captioned weekly with this technology. This article describes the creation and broadcast of captions in real time, including equipment, staffing, and procedures.