KALMYK FOLK TALE IN A FOREIGN LANGUAGE TRANSLATION (ON THE EXAMPLE OF THE ANALYSIS OF ORIGINAL AND TRANSLATED TEXTS OF FAIRY TALE "AYU CHIKTE AND AVKHA TSETSEN")

Исследование посвящено сравнительному анализу оригинального и переводных текстов калмыцкой народной сказки «Аю Чикт Авха Цецен хойр» («Аю Чикте и Авха Цецен») из репертуара сказителя М. Буринова. В процессе сличения исходного текста сказки на калмыцком языке (1960) и русскоязычного перевода М. Г. Ватагина (1964) отмечается характер разночтений и неточностей, обнаруженных в иноязычном нарративе в передаче смысла отдельных эпизодов сюжета, формульных выражений, словосочетаний, играющих важную роль в сказочном повествовании. Изучение фольклорного текста в его разноязычных воплощениях представляется актуальным в свете проблем, возникающих при взаимодействии текстов дистантных культур. Для передачи национальной специфики сказочной традиции требуется максимальная точность при переводе, имеющим важное значение для понимания исконного смысла оригинального текста. The study is devoted to a comparative analysis of the original and translated texts of the Kalmyk folk tale "Ayu Chikt Avkha Tsetsn khoir" ("Ayu Chikte and Avkha Tsetsen") from the repertoire of the narrator M. Burinov. In the process of comparing the original text of the fairy tale in the Kalmyk language (1960) and the Russian translation by M. G. Vatagina (1964) notes the nature of the discrepancies and inaccuracies found in the foreign language narrative in the transfer of the meaning of individual episodes of the plot, formula expressions, word combinations), which play an important role in the fairy tale narration. The study of a folklore text in its multilingual embodiments is relevant in the light of the problems that arise within the interaction of texts of distant cultures. To convey the national specifics of the fairy - tale tradition, maximum accuracy is required when translating episodes, formulas and some words that are important for understanding the original meaning of an original text.

KALMYK FOLK TALE IN A FOREIGN LANGUAGE TRANSLATION (ON THE EXAMPLE OF THE ANALYSIS OF ORIGINAL AND TRANSLATED TEXTS OF FAIRY TALE "AYU CHIKTE AND AVKHA TSETSEN")

Исследование посвящено сравнительному анализу оригинального и переводных текстов калмыцкой народной сказки «Аю Чикт Авха Цецен хойр» («Аю Чикте и Авха Цецен») из репертуара сказителя М. Буринова. В процессе сличения исходного текста сказки на калмыцком языке (1960) и русскоязычного перевода М. Г. Ватагина (1964) отмечается характер разночтений и неточностей, обнаруженных в иноязычном нарративе в передаче смысла отдельных эпизодов сюжета, формульных выражений, словосочетаний, играющих важную роль в сказочном повествовании. Изучение фольклорного текста в его разноязычных воплощениях представляется актуальным в свете проблем, возникающих при взаимодействии текстов дистантных культур. Для передачи национальной специфики сказочной традиции требуется максимальная точность при переводе, имеющим важное значение для понимания исконного смысла оригинального текста. The study is devoted to a comparative analysis of the original and translated texts of the Kalmyk folk tale "Ayu Chikt Avkha Tsetsn khoir" ("Ayu Chikte and Avkha Tsetsen") from the repertoire of the narrator M. Burinov. In the process of comparing the original text of the fairy tale in the Kalmyk language (1960) and the Russian translation by M. G. Vatagina (1964) notes the nature of the discrepancies and inaccuracies found in the foreign language narrative in the transfer of the meaning of individual episodes of the plot, formula expressions, word combinations), which play an important role in the fairy tale narration. The study of a folklore text in its multilingual embodiments is relevant in the light of the problems that arise within the interaction of texts of distant cultures. To convey the national specifics of the fairy - tale tradition, maximum accuracy is required when translating episodes, formulas and some words that are important for understanding the original meaning of an original text.

Pakistan sign language translation tool in educational setting: teachers perspective

PurposeThe current study aims to determine the viability of the tool developed by Abbas and Sarfraz (2018) to translate English speech and text to Pakistan Sign Language (PSL) with bilingual subtitles.Design/methodology/approachFocus group interviews of 30 teachers of a Pakistani private university were conducted; who used the PSL translation tool in their classrooms for lecture delivery and communication with the deaf students.FindingsThe findings of the study determined the viability of the developed tool and showed that it is helpful in teaching deaf students efficiently. With the availability of this tool, teachers are not dependent on human sign language (SL) interpreters in their classrooms.Originality/valueOverall, this tool is an effective addition to educational technology for special education. Due to the lack of Sign Language (SL) understanding, learning resources and availability of human SL interpreters in Pakistan, institutions feel dependency and scarcity to educate deaf students in a classroom. Unimpaired people and especially teachers face problems communicating with deaf people to arrange one interpreter for a student(s) in multiple classes at the same time which creates a communication gap between a teacher and a deaf student.

Improved neural machine translation for low-resource English–Assamese pair

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

Analytical review of models and methods for automatic recognition of gestures and sign languages

Introduction: Currently, the recognition of gestures and sign languages is one of the most intensively developing areas in computer vision and applied linguistics. The results of current investigations are applied in a wide range of areas, from sign language translation to gesture-based interfaces. In that regard, various systems and methods for the analysis of gestural data are being developed. Purpose: A detailed review of methods and a comparative analysis of current approaches in automatic recognition of gestures and sign languages. Results: The main gesture recognition problems are the following: detection of articulators (mainly hands), pose estimation and segmentation of gestures in the flow of speech. The authors conclude that the use of two-stream convolutional and recurrent neural network architectures is generally promising for efficient extraction and processing of spatial and temporal features, thus solving the problem of dynamic gestures and coarticulations. This solution, however, heavily depends on the quality and availability of data sets. Practical relevance: This review can be considered a contribution to the study of rapidly developing sign language recognition, irrespective to particular natural sign languages. The results of the work can be used in the development of software systems for automatic gesture and sign language recognition.

Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network

The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing “Within Blocks” and “Before Classifier” methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models’ efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet “Before Classifier” models are more efficient than “Within Blocks” CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the “Before Classifier” of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.

Sentence Augmentation for Language Translation Using GPT-2

Data augmentation has recently become an important method for improving performance in deep learning. It is also a significant issue in machine translation, and various innovations such as back-translation and noising have been made. In particular, current state-of-the-art model architectures such as BERT-fused or efficient data generation using the GPT model provide good inspiration to improve the translation performance. In this study, we propose the generation of additional data for neural machine translation (NMT) using a sentence generator by GPT-2 that produces similar characteristics to the original. BERT-fused architecture and back-translation are employed for the translation architecture. In our experiments, the model produced BLEU scores of 27.50 for tatoebaEn-Ja, 30.14 for WMT14En-De, and 24.12 for WMT18En-Ch.

Geoscience Language Processing for Exploration

Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.

TEACHING SCREENWRITINGAS TRANSLATION AND ADAPTATION:CRITICAL REFLECTIONS --©2021 P. CATTRYSSE

This chapter discusses teaching screenwriting in terms of translation and adaptation. Since translation and adaptation scholars often use both terms interchangeably to signify semiosis or culture, section one suggests some more specific working definitions. Realigning terminology with everyday language, translation is redefined as an invariance-based phenomenon while adaptation is reconceived as a variance-based phenomenon, which entails better fit. More specific working definitions help at once specifying what one could be teaching or learning in more precise terms.<br><br>Definitional issues involve conceptual and epistemic boundaries, which stakeholders use to defend their interests. This ushers in section two, which discusses the current Western Romantic view on art and culture, and how having driven a rift between art and craft, it opposes the aforesaid conceptual boundaries, and disparages screenwriting, translation, and adaptation, lest they comply with the Romantic rule. Suggestions follow to re-open the Romantic view to its pre-Romantic stance, and to revalue both art and craft values in screenwriting, translation and adaptation.<br><br>Section three concludes with some caveats. Since it took Romanticism half a millennium to form and segregate its proper socio-cultural and economical tribes, nudging it back to its wider pre-Romantic views is not likely to succeed in the near future.<br>