Automatic Translation Of Text Research Articles

A Study on the Translation of Cultural Classics Based on Deep Learning Methods

China’s cultural classics have high artistic and ideological values in the world, which implies China’s historical heritage and the inheritance of the cultural situation of the Chinese nation for thousands of years. At present, with the rapid development of China’s economy, especially in the development environment of cultural globalization, China’s traditional cultural classics have attracted worldwide attention along with historical and cultural treasures. In fact, other countries need to translate cultural classics out of their love for China’s cultural classics or academic research. However, there are a large number of cultural classics in China. According to relevant data, there are approximately 35,000 kinds of cultural classics, out of which only 0.2% are translated into foreign languages. In order to make China’s excellent cultural classics known to the world and let more people in other countries understand Chinese culture, this paper studies the translation of cultural classics through in-depth learning. Firstly, this paper introduces the basic concept of deep learning and proposes an algorithm that deeply studies the convolution layer and pool layer. Secondly, the algorithm establishes a deep learning model that calculates and counts the text information of ancient books through explicit intertextuality. Thirdly, the model carries out automatic text translation and lists the analysis process of cultural classics translation based on intertextuality, so as to study cultural classics translation. The obtained results can promote the development of cultural classics translation in China. For the translation of complex cultural classics, the effects of the three models are tested experimentally, of which the sequence model (seq) is the best. This model is fast and simple to extract the text of literary classics.

AUTOMATIC TEXT TRANSLATION SYSTEM FOR ARTIFICIAL LLANGUAGES

The growing number and variety of artificial languages leads to the need and relevance of creating automatic dictionaries for their translation in order to facilitate human communication. Such languages include languages where vocabulary, phonetics, and grammar have been specifically designed to achieve specific goals and to communicate with a group of people by interests or place of residence. These languages can be distributed among people of certain professions or among neighboring nations. Examples are slang and surzhik. The common for them is that there is a basic language (literary), the intersection in spelling and meaning of words and phrases with which is quite large. The main goal of the project is to create a system of automatic translation of words and texts from / into arbitrary languages, including hybrid, artificial and slang ones. The proposed model shows the interaction and partial interdependence of the creation and adjustment modules and the translation module of the dictionary, which is explained by tacking the approach of reverse propagation of the translation error. To perform experiments and analyze the performance of the proposed approach to the organization of automatic translation of texts from and into arbitrary language, a software application was developed, which includes a subprogram of initial word processing for dictionary organization, one for creating a working dictionary and one for two-way improvement of created dictionary by the inclusion of new texts in order to improve the quality of translation, including the search for word phrases, idiom, and translation for them, the subprogram of dividing the dictionary into sub-dictionaries with a small percentage of text, the subprogram of the translator itself. To test and analyze the results of the proposed generalized model, three types of source texts were used: literary poetry translation, literary prose translation, literal prose translation. The results of the experiments showed that the proposed approach provides a high level of translation (up to 98,8%) in similar languages (between such languages as Ukrainian-Russian, or Ukrainian - Ukrainian-Russian surzhik wih equal word order in the sentence), especially with a literally translated source text. It has become known that the use of artistic texts to generate dictionaries is possible, but not very effective.

МАТЕМАТИЧНІ МОДЕЛІ ТА ПІДХОДИ ДО РОЗВ'ЯЗАННЯ ОПТИМІЗАЦІЙНИХ ЗАДАЧ ШТУЧНОГО ІНТЕЛЕКТУ

До оптимізаційних задач штучного інтелекту відносять задачі розпізнавання (образів, мовлення), задача клінічної діагностики, порівняння текстів на плагіат, автоматичний переклад текстів з однієї мови на іншу, класифікація тощо. Для реалізації цих задач автоматизованим способом необхідно адекватно сформулювати їхню математичну модель. Незважаючи на те, що цій проблемі присвячено багато робіт, точної математичної постановки, яка б дала змогу розробляти ефективні для їхнього розв’язання алгоритми, ще не розроблено. Також для них не змодельовано цільової функції в явному вигляді. До того ж одержаний глобальний розв’язок за розробленою цільовою функцією не завжди збігається з метою дослідження. Це пов’язано з тим, що характерною особливістю цих задач є наявність ситуації невизначеності, що ускладнює як їхнє моделювання, так і розв’язання. Для моделювання задач цього класу використовують різні підходи, зокрема стохастичні, логіко-лінгвіністичні методи, моделі Маркова, лінійне цілочислове програмування, теорію розпізнавання образів, нейронні мережі. Для їхньго вирішення використовують швидкий метод розповсюдження обмежень, евристичні методи. Під евристичними алгоритмами, як правило, розуміють способи прийняття рішень подібні до того, як це робить людина, та побудовані на інтуїтивних міркуваннях, що спираються на попередній досвід. Використання евристичних алгоритмів поширене в задачах розпізнавання різної природи. Для багатьох практичних проблем ці алгоритми чи не єдино можливий шлях для отримання задовільного рішення в реальному часі. Значна частина прикладних задач штучного інтелекту в процесі їхнього розв’язання потребують перебору варіантів, що вказує на їхню комбінаторну природу. Тому ці задачі зводяться до задач комбінаторної оптимізації. Відомі методи моделювання не завжди пояснюють перебірну природу задач штучного інтелекту. У даній роботі для деяких задач цього класу побудовано математичні моделі з використанням теорії комбінаторної оптимізації. Показано, що аргументом цільової функції є комбінаторні конфігурації різних типів.

Cadres, scripts, registres — complexité de traduction des mots polysémiques

The article deals with issues related to the description of polysemic units for supervised automatic translation of texts from French to Polish based on the object-oriented method by W. Banyś (AOO). Applying to the description additional tools allowing for the disambiguation — frames, scripts and register analysis — the author focuses on presenting the complexity of the description of lexical units. It should be stressed that the decision to assign meanings of such words one or another equivalents in the target language is determined by the criterion of preference. Some of the meanings of the word are easy to describe in such a way that the program can make a correct translation into the target language with a high degree of probability. However, there are often such meanings whose description is much more complex. The closer the meanings of the disambiguated words in the original and target language and the more similar the communication situation in which they are used, the more difficult it is to clearly indicate the boundaries between the meanings. In such cases, there is a much greater range of preferential character of the translation, which we have to adopt The French noun conjunction (f), chosen for analysis, reflects this phenomenon very well.

BRAPT: A New Metric for Translation Evaluation Based on Psycholinguistic Perspectives

There are some metrics to evaluate automatic text translations in the literature. However, the state-of-the-art of these metrics still has limitations. One of them is the dependence of an exact and ordered pairing of words for evaluating similarity among texts. Another, is the non-consideration of the semantics of the text in such comparison. Previous studies point out the need to analyze the semantics of words in the evaluation of translations. In this scenario, this paper presents a novel metric capable of evaluating the differences in automatic text translations that takes into account the semantics of the words presented in the texts. As a proof of concept, we selected ten journalistic texts written in English. These texts have been translated to Portuguese by specialists and by automatic text translation tools. Experimental results show the potential of the proposed metric in evaluating these translations, indicating it can perform better than the state-of-the-art metric.

SEQ2SEQ-VIS : A Visual Debugging Tool for Sequence-to-Sequence Models.

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.

De la désambiguïsation d’éducation ou de l’éducation de la désambiguïsation

The paper is an attempt to disambiguate into Polish the polysemous French noun éducation for the purpose of automatic translation of texts. The analysis of the noun is carried out on the basis of the Object Oriented Approach proposed by W. Banyś. The Author searches the broad and narrow context of the noun and applies the criteria of scripts and frames, by means of which the meanings of the noun are described.

모바일 이미지 기반의 문자인식 시스템

Recently, due to the development of mobile phone and supply of smart phone, many contents have been developed. Especially, since the small-sized cameras are equiped in mobile devices, people are interested in the image based contents development, and it also becomes important part in their practical use. Among them, the character recognition system can be widely used in the applications such as blind people guidance systems, automatic robot navigation systems, automatic video retrieval and indexing systems, automatic text translation systems. Therefore, this paper proposes a system that is able to extract text area from the natural images captured by smart phone camera. The individual characters are recognized and result is output in voice. Text areas are extracted using Adaboost algorithm and individual characters are recognized using error back propagated neural network.

Steps Toward Knowledge-Based Machine Translation

This paper considers the possibilities for knowledge-based automatic text translation in the light of recent advances in artificial intelligence. It is argued that competent translation requires some reasonable depth of understanding of the source text, and, in particular, access to detailed contextual information. The following machine translation paradigm is proposed. First, the source text is analyzed and mapped into a language-free conceptual representation. Inference mechanisms then apply contextual world knowledge to augment the representation in various ways, adding information about items that were only implicit in the input text. Finally, a natural-language generator maps appropriate sections of the language-free representation into the target language. We discuss several difficult translation problems from this viewpoint with examples of English-to-Spanish and English-to-Russian translations; and illustrate possible solutions as embodied in a computer understander called SAM, which reads certain kinds of newspaper stories, then summarizes or paraphrases them in a variety of languages.