Enhancing Lexical Translation Consistency for Document-Level Neural Machine Translation

Document-level neural machine translation (DocNMT) has yielded attractive improvements. In this article, we systematically analyze the discourse phenomena in Chinese-to-English translation, and focus on the most obvious ones, namely lexical translation consistency. To alleviate the lexical inconsistency, we propose an effective approach that is aware of the words which need to be translated consistently and constrains the model to produce more consistent translations. Specifically, we first introduce a global context extractor to extract the document context and consistency context, respectively. Then, the two types of global context are integrated into a encoder enhancer and a decoder enhancer to improve the lexical translation consistency. We create a test set to evaluate the lexical consistency automatically. Experiments demonstrate that our approach can significantly alleviate the lexical translation inconsistency. In addition, our approach can also substantially improve the translation quality compared to sentence-level Transformer.

Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages

Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.

Japanese Translation Quality Evaluation System Based on Deep Neural Network Algorithm

The current translation quality evaluation system relies on the combination of manual and text comparison for evaluation, which has the defects of low efficiency and large evaluation errors. In order to optimize the defects of the current quality evaluation system, a Japanese translation quality evaluation system based on deep neural network algorithm will be designed. In order to improve the processing efficiency of the system, the USB3.0 communication module of the hardware system will be optimized. Based on the hardware design, the reference translation map is used to extend the reference translation of Japanese translation. The evaluation indexes of over- and under-translation are set, and the evaluation of Japanese translation quality is realized after the parameters are determined by training the deep neural network using the sample set. The system functional test results show that the average data transmission processing time of the system is improved by about 31.27%, and the evaluation error interval is smaller and the evaluation is more reliable.

Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation

Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable improvements in translation quality, as assessed by a limited set of automatic evaluation metrics. In this study, we extend this evaluation by calculating a wider range of automated quality metrics that tap into different aspects of translation quality and by performing manual MT error analysis. Moreover, we investigate in more detail how fuzzy matches influence translations and where potential quality improvements could still be made by carrying out a series of quantitative analyses that focus on different characteristics of the retrieved fuzzy matches. The automated evaluation shows that the quality of NFR translations is higher than the NMT baseline in terms of all metrics. However, the manual error analysis did not reveal a difference between the two systems in terms of total number of translation errors; yet, different profiles emerged when considering the types of errors made. Finally, in our analysis of how fuzzy matches influence NFR translations, we identified a number of features that could be used to improve the selection of fuzzy matches for NFR data augmentation.

To Domesticate or to Foreignize: An Approach to Translating Fables and Fairy Tales

The present study deals with the strategies used in the Arabic translations of the most popular genres of children’s literature; namely fairy tales and fables as an attempt to identify the best methods and strategies to be adopted in translating these genres to fulfill the ultimate purpose of enriching the children’s knowledge in addition to attracting their interest and arousing the joy sought for in every piece of literature. The study sets off from three dominating trends: the first calls for the adoption of domestication strategy of translation as the most appropriate and effective strategy in translation for children. In the same line, the second opposes using the foreignization strategy, while the third trend advocates for the joint employment of various strategies to fulfill certain requirements and needs that would be called upon within the context such as didactic purposes. Throughout the process of examining and verifying the theses of these trends, samples of translations of the genres are chosen based on the most popular and well known fairy tales and fables either circulated in written form or televised as movies or cartoons; namely Hans Christian Andersen’s Fairy Tales and Aesop’s Fables. These samples are subjected to translation quality assessment to come out with a quality statement to highlight their merits and demerits. The receptors’ (children) impact is also sought via conducting a field study that has been designed for children of two age groups defined by specialized scholars as intended receptors of the genre

Parallel Algorithm of Hierarchical Phrase Machine Translation Based on Distributed Network Memory

Machine translation has developed rapidly. But there are some problems in machine translation, such as good reading, unable to reflect the mood and context, and even some language machines can not recognize. In order to improve the quality of translation, this paper uses the SSCI method to improve the quality of translation. It is found that the translation quality of hierarchical phrases is significantly improved after using the parallel algorithm of machine translation, which is about 9% higher than before, and the problem of context free grammar is also solved. The research also found that the use of parallel algorithm can effectively reduce the network memory occupation, the original 10 character content, after using the parallel algorithm, only need to occupy 8 characters, the optimization reaches 20%. This means that the parallel algorithm of hierarchical phrase machine translation based on distributed network memory can play a very important role in machine translation.

TECHNIQUE AND QUALITY TRANSLATION OF IDHAFI PHRASES IN THE POETRY OF ASYHADU AN LA IMRAATA ILLA ANTI BY NIZAR QABBANI

The translation process is an activity of transferring one language to another, from the translation process the researcher examines the translation techniques and translation quality of the collection of translation poems by Nizar Qabbani entitled Asyhadu An La Imraata Illa Anti. The methodological foundation that the researcher uses is a qualitative method. In this study, the researcher found 160 Idhafi phrases which were translated using 15 translation techniques, and 2 translation techniques that had good quality in conveying the intent, message, and purpose to the reader into the target language. That is, the equivalent technique generally has the highest value on three aspects of assessment for the quality of translation, among others, the accuracy aspect has a score of 83.3%, then the acceptability aspect has a score of 85.7%, and in the readability aspect, it has a score of 78.6% for further research. The researcher can discuss in more detail the translation method of Nizar Qabbani's Poetry

Pragmatic Function Shifts in the Translation of WH Questions in “500 Days of Summer”

<p>The author is analyzing and conducting a translation research in interrogative utterances from the film "500 Days of Summer" especially WH questions, because WH questions not only contain information at the surface structure, but also pragmatic functions within them. This study is being carried out in order to help the writer and readers understand interrogative sentences more clearly. This research is conducted under Translational research using a descriptive qualitative method. The analysis applies the theory of Nida &amp; Taber’s translation theory, Newmark’s translation theory, Mona Baker’s translation techniques theory, Marcella Frank’s interrogative sentences theory and Nababan, Nuraeni &amp; Sumardiono’s translation quality assessment theory.</p>The scope of this research is a translational analysis of a film subtitle. This research focuses on the translation of interrogative utterances in the form of WH questions in the film "500 Days of Summer" specifically character dialogues. This research intends to investigate the pragmatic function of the utterances on the target language, as well as the pragmatic differences that may exist between the source and target languages, as well as the translator's translation techniques, including their impact on translation quality.

Translation quality management in the AI Age. New technologies to perform translation quality assurance operations

This article presents a selection of some of the latest technologies to perform translation quality management operations such as quality assurance and translation quality assessment using artificial intelligence and machine learning; it also discusses the impact of these technological advances in the latest academic research trends and the translation industry.