Text Machine Translation Research Articles

Retraction Note: Analysis and evaluation on the quality of news text machine translation based on neural network

Retraction Note: Analysis and evaluation on the quality of news text machine translation based on neural network

Artificial intelligence and human translation: A contrastive study based on legal texts

Artificial intelligence has advanced significantly in recent years, affecting multiple aspects of life. In particular, this has had an impact on the machine translation of texts, reducing or removing human interaction. Artificial intelligence (AI)-based translation software models have thus become widely available, and these now include Google Translate, Bing, Microsoft Translator, DeepL, Reverso, Systran Translate, and Amazon Translate. Several computer-aided translation (CAT) tools such as Memoq, Trados, Smartcat, Lokalise, Smartling, Crowdin, TextUnited, and Memsource are also available. More recently, artificial intelligence has been applied in the development of applications such as ChatGPT, ChatSonic, GPT-3 Playground, Chat GPT 4 and YouChat, which simulate conversational responses to researchers' inquiries, mimicking human interactions more directly. This study thus aimed to examine any remaining contrasts between human and AI translation in the legal field to investigate the potential hypothesis that there is now no difference between human and AI translation. The paper thus also examined concerns about whether the need for human translators will decline in the face of AI development, as well as beginning to assess whether it will ever be possible for those in the legal field to depend only on machine translation. To achieve this, a collection of legal texts from various contracts was chosen, and these pieces were both allocated to legal translators and subjected to AI translation systems. Using a contrastive methodology, the study thus examined the differences between AI and human translation, examining the strengths and weaknesses of both approaches and discussing the situations in which each approach might be most effective.

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.

Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including (1) clinical case (CC), (2) clinical terminology (CT), and (3) ontological concept (OC) show that our models achieved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) outperformed the other two extra-large language models by a large margin in the clinical domain fine-tuning, which finding was never reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformation. Our data is openly available for research purposes at: https://github.com/HECTA-UoM/ClinicalNMT.

PROBLEMS OF PSYCHOLOGICAL SCIENTISIC TEXTS MACHINE TRANSLATION ON THE MATERIAL OF AN INFORMATIONAL BROCHURE

The article is devoted to the analysis of the psychological scientific text machine translation (from the English language to the Russian one). On the material of the Russian machine translation of an informational brochure, potential difficulties were identified. The author points out the mistakes in machine translation and offers ways to solve them. A particular attention is paid to the translation of special terminology of the psychological semantic field.
 Background. Machine translation programs are often used as a way of getting texts translation. On the one hand, it is an available inexpensive way to get a quick translation. On the other hand, there is no guarantee of translation adequacy. The basis of quality translation analysis was a machine translation of scientific text.
 Purpose. On the basis of the text made by the machine translation program some mistakes are revealed and analyzed, some ways to solve them are offered. Special attention is paid to the translation of terms in the field of psychology.
 Materials and methods. The research material was a machine translation of the brochure “Anxiety Disorders in Children” written by the American Anxiety Disorders Association (the translation is from English into Russian). The translation was made with the help of the online program Google Translator.
 Results. Scientific texts on psychology have become popular in the world, but as the analysis showed, their translation is still difficult for online translation programs. Machine translation is not the way to get an equivalent text, but it does make the translation process easier. At the same time, an interpreter should spend time on further text editing. One type of mistakes is connected with the translation of terms. Besides there are mistakes caused by the difference between the grammatical structures of the original language and the translation language, such as the voice constructions, special word orders and set phrases.

MULTIMODAL SPEECH RECOGNITION BASED ON AUDIO AND TEXT DATA

Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.

Image Visual Attention Mechanism-based Global and Local Semantic Information Fusion for Multi-modal English Machine Translation

<p>Machine translation is a hot research topic at present. Traditional machine translation methods are not effective because they require a large number of training samples. Image visual semantic information can improve the effect of the text machine translation model. Most of the existing works fuse the whole image visual semantic information into the translation model, but the image may contain different semantic objects. These different local semantic objects have different effects on the words prediction of the decoder. Therefore, this paper proposes a multi-modal machine translation model based on the image visual attention mechanism via global and local semantic information fusion. The global semantic information in the image and the local semantic information are fused into the text attention weight as the image attention. Thus, the alignment information between the hidden state of the decoder and the text of the source language is further enhanced. Experimental results on the English-German translation pair and the Indonesian-Chinese translation pair on the Multi30K dataset show that the proposed model has a better performance than the state-of-the-art multi-modal machine translation models, the BLEU values of English-German translation results and Indonesian-Chinese translation results exceed 43% and 29%, which proves the effectiveness of the proposed model.</p> <p> </p>

A New Concept of Electronic Text Based on Semantic Coding System for Machine Translation

In the field of machine translation of texts, the ambiguity in both lexical (dictionary) and structural aspects is still one of the difficult problems. Researchers in this field use different approaches, the most important of which is machine learning in its various types. The goal of the approach that we propose in this article is to define a new concept of electronic text, which makes the electronic text free from any lexical or structural ambiguity. We used a semantic coding system that relies on attaching the original electronic text (via the text editor interface) with the meanings intended by the author. The author defines the meaning desired for each word that can be a source of ambiguity. The proposed approach in this article can be used with any type of electronic text (text processing applications, web pages, email text, etc.). Thanks to the approach that we propose and through the experiments that we have conducted using it, we can obtain a very high accuracy rate. We can say that the problem of lexical and structural ambiguity can be completely solved. With this new concept of electronic text, the text file contains not only the text but also with it the true sense of the exact meaning intended by the writer in the form of symbols. These semantic symbols are used during machine translation to obtain a translated text completely free of any lexical and structural ambiguity.

ОПРЕДЕЛЕНИЕ НАБОРА УСЛОВИЙ ДЛЯ АВТОМАТИЧЕСКОГО ПОИСКА ОПТИМАЛЬНОГО ВАРИАНТА ГИБРИДНОГО МАШИННОГО ПЕРЕВОДА ТЕКСТА НА ГРАФЕМНОМ УРОВНЕ

The article is devoted to the Algorithmic Search for Optimal Solutions for evaluating andimproving the Quality of Hybrid Machine Translation of Text. The Object of the Research is Textson any Alphabetical Languages with different Bases (Alphabets), as well as their Translations intoother Alphabetical Languages.Currently, existing Methods and Means of Hybrid Machine Translationare characterized by a wide variety of Quality Assessment Algorithms, but the Disadvantageof these Methods is that most of them do not have Clear Criteria, Limitations and Schemes of Assessments,eventually, the Result of the Translation in most cases does not correspond to the Levelof Publication. The Aim of the Work is to determine a Set of Conditions for automatic search forthe Best Option of Hybrid Machine Translation of Text at the Level of Graphemes.The Main Tasksto be solved during the Research are the Search for Qualitative and Quantitative Conditions, includingthe maximum, minimum and average values of the Lengths of Translations, Reverse Translationsand Editorial Distances between Pairs of Texts that have the Same Meaning. The ScientificNovelty lies in use the Graphical Representation of the Model of Alphabetic Languages at theLevel of Graphemes in the Form of a Cartesian Coordinate System with a Dimension equal to aUnit Editorial Distance (by Levenstein). When solving the de Goui’s Theorem, the current Rules ofStandardization PR 50.1.027–2014 "Rules for the Provision of Translation and Special Types ofLinguistic Services", the Method of Decanonicalization and the Model "Original Text – Translation– Reverse Translation" were used. As a Result, Actual and Practically Applicable Solutionsfor the Problems under consideration are obtained. In this regard; this Work may be interest to awide range of Specialists engaged in Machine Translation and Translation Studies.

Machine Phraseological Translation of Scientific-Technical Texts Based on the Model of Generalized Syntagmas

This article proposes a new solution to the problem of phraseological machine translation of texts based on the technology of “semantic translation constructor.” This technology is based on the concept of generalized syntagmas, which is based on the proposition that the syntactic structure of a sentence or its fragments (syntactic constructions) can be represented as a sequence of generalized syntagmas that reflect the grammar properties of specific text words. On the basis of this concept the source sentence can be transformed into a set of templates of syntactic constructions accompanied by their “joint assemblies.” Then, the translation process can be performed by “disassembling” the source sentence into templates of syntactic constructions, correlating these source templates with templates of their translated equivalents and final “assembly” of the translated sentence in accordance with “technological instructions” in the “assembly rules.” This article proposes mechanisms for the automatic formation of a template representation of the hierarchy of syntactic constructions of bilingual sentences.

Analysis and evaluation on the quality of news text machine translation based on neural network

Machine translation quality estimation is an important task in natural language processing. Unlike traditional automatic machine translation evaluation methods, the quality of machine translation is evaluated by the translation quality estimation method without using manual reference translation. In view of the fact that the feature extraction of current sentence-level machine translation quality estimation relies heavily on linguistic analysis, which leads to insufficient generalization ability and restricts the performance of subsequent support vector regression algorithm, it is proposed to extract the features of sentence vectors by using contextual word prediction model and matrix decomposition model in deep learning, and combine them with the features of recurrent neural network language model to improve the correlation between automatic estimation of translation quality and manual evaluation. The experimental results on WMT15 and WMT16 translation quality estimation subtask data sets show that the performance statistics of the method of extracting sentence vector features by adopting context word prediction model are consistently superior to that of the traditional Quest method and continuous space language model sentence vector feature extraction method. It reveals that the proposed feature extraction method not only requires no linguistic analysis, but also significantly improves the effect of translation quality estimation.

Problems of machine translation of business texts from Russian into English

This article draws on the example of business texts to consider practical aspects of the distortion of meaning in translation from one language to another in the available machine translation (MT) systems and their underlying approach based on word-by-word translation. An integrated functional approach to translating business texts is suggested on the basis of analyzing semantic and morphological features of actual text content and also on axiological and epistemic semantic features that bring to light subjective modality. The suggested technique is used to develop an algorithm of business text MT that makes it possible to resolve the word-by-word translation issue and conveys the meanings of short texts. Cases of testing the suggested technique and the derived algorithm are considered for the Russian–English language pair.

Analysing machine translation

In the era of information technology, when the world has been striving towards electronic communication, when everyday conversations are replaced by e-mail, and habits become unthinkable without the computer, then the manner of performing all the tasks changes and the way in which texts are translated. In the new age all are translators, and think they are professional enough to deal with the process of translation. The easy availability of machine (computer) translation has reduced it to the level of 'copy-paste'. This paper will analyse machine translation of Andric's short story 'Kids'. We will use Google translate software for machine translation of texts, because it is the most accessible and most commonly used as it is free. Due to the limited length of this paper, we will only analyse one of a large number of disadvantages that can occur in such a translation, in order to show that though some things still cannot be left to machines, and that man is better able to tackle the not so easy challenge of translating a literary work.

Evaluating Arabic to English Machine Translation

Online text machine translation systems are widely used throughout the world freely. Most of these systems use statistical machine translation (SMT) that is based on a corpus full with translation examples to learn from them how to translate correctly. Online text machine translation systems differ widely in their effectiveness, and therefore we have to fairly evaluate their effectiveness. Generally the manual (human) evaluation of machine translation (MT) systems is better than the automatic evaluation, but it is not feasible to be used. The distance or similarity of MT candidate output to a set of reference translations are used by many MT evaluation approaches. This study presents a comparison of effectiveness of two free online machine translation systems (Google Translate and Babylon machine translation system) to translate Arabic to English. There are many automatic methods used to evaluate different machine translators, one of these methods; Bilingual Evaluation Understudy (BLEU) method. BLEU is used to evaluate translation quality of two free online machine translation systems under consideration. A corpus consists of more than 1000 Arabic sentences with two reference English translations for each Arabic sentence is used in this study. This corpus of Arabic sentences and their English translations consists of 4169 Arabic words, where the number of unique Arabic words is 2539. This corpus is released online to be used by researchers. These Arabic sentences are distributed among four basic sentence functions (declarative, interrogative, exclamatory, and imperative). The experimental results show that Google machine translation system is better than Babylon machine translation system in terms of precision of translation from Arabic to English.