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We present a novel approach to automatic Sign Language Production using recent developments in Neural Machine Translation (NMT), Generative Adversarial Networks, and motion generation. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations for training. We achieve this by breaking down the task into dedicated sub-processes. We first translate spoken language sentences into sign pose sequences by combining an NMT network with a Motion Graph. The resulting pose information is then used to condition a generative model that produces photo realistic sign language video sequences. This is the first approach to continuous sign video generation that does not use a classical graphical avatar. We evaluate the translation abilities of our approach on the PHOENIX14T Sign Language Translation dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities of our approach for both multi-signer and high-definition settings qualitatively and quantitatively using broadcast quality assessment metrics.

Over recent years, machine translation has achieved astounding accomplishments. Machine translation has become more evident with the need to understand the information available on the internet in different languages and due to the up-scaled exchange in international trade. The enhanced computing speed due to advancements in the hardware components and easy accessibility of the monolingual and bilingual data are the significant factors that have added up to boost the success of machine translation. This paper investigates the machine translation models developed so far to the current state-of-the-art providing a solid understanding of different architectures with the comparative evaluation and future directions for the translation task. Because hybrid models, neural machine translation, and statistical machine translation are the types of machine translation that are utilized the most frequently, it is essential to have an understanding of how each one functions. A comprehensive comprehension of the several approaches to machine translation would be made possible as a result of this. In order to understand the advantages and disadvantages of the various approaches, it is necessary to conduct an in-depth comparison of several models on a variety of benchmark datasets. The accuracy of translations from multiple models is compared using metrics such as the BLEU score, TER score, and METEOR score.

Arabic Machine Translation (MT) has gained considerable attention from the research community due to the widespread use of Arabic as one of the world’s major languages. While significant progress has been made in this field, the quality of Arabic MT systems still lags behind that of some other languages. This survey paper aims to provide a comprehensive overview of Arabic MT by addressing its challenges, highlighting previous research studies, and discussing the field’s current state. The survey begins by introducing the characteristics of the Arabic language and the specific challenges it poses for translation. It then summarises the key research studies and explores the available tools and resources for building and evaluating Arabic MT systems. Additionally, the survey examines the strengths and weaknesses of existing techniques used in Arabic-English (and English-Arabic) machine translation, focusing on neural machine translation (NMT) approaches. By comparing different NMT methods and addressing linguistic and technical challenges, this paper offers valuable insights for researchers and professionals in Arabic MT. The findings, critiques, and open issues presented in this survey can serve as a foundation for further research and improvement in Arabic MT, providing a valuable resource for those interested in advancing the field.

Context-aware machine translation approaches improve the quality of translation by incorporating the context of the surrounding phrases in the translation of a phrase. So far, for the low-resource language pair English-Amharic, context-aware machine translation approaches have not been investigated in depth. Moreover, the current approaches for machine translation of the low-resource language pair English-Amharic usually require a large set of parallel corpus to achieve fluency. This research investigates a new approach that translates English text to Amharic text using a combination of context based machine translation (CBMT) and a recurrent neural network machine translation (RNNMT). We built a bilingual dictionary for the CBMT to use along with a target corpus. The RNNMT model is then provided with the output of the CBMT and a parallel corpus for training. The approach is evaluated using the New Testament Bible as a corpus. Our combinational approach on English–Amharic language pair yields a performance improvement over the simple neural machine translation (NMT), while no improvement is seen over CBMT for a small dataset. We have also assessed the impact of the dictionary used by CBMT on the overall performance of the approach. The result shows that the dictionary accuracy, and hence, the CBMT output is found to affect the combinational approach.

This article presents the most up-to-date, influential automated, semiautomated and human metrics used to evaluate the quality of machine translation (MT) output and provides the necessary background for MT evaluation projects. Evaluation is, as repeatedly admitted, highly relevant for the improvement of MT. This article is divided into three parts: the first one is dedicated to automated metrics; the second, to human metrics; and the last, to the challenges posed by neural machine translation (NMT) regarding its evaluation. The first part includes reference translation–based metrics; confidence or quality estimation (QE) metrics, which are used as alternatives for quality assessment; and diagnostic evaluation based on linguistic checkpoints. Human evaluation metrics are classified according to the criterion of whether human judges directly express a so-called subjective evaluation judgment, such as ‘good’ or ‘better than’, or not, as is the case in error classification. The former methods are based on directly expressed judgment (DEJ); therefore, they are called ‘DEJ-based evaluation methods’, while the latter are called ‘non-DEJ-based evaluation methods’. In the DEJ-based evaluation section, tasks such as fluency and adequacy annotation, ranking and direct assessment (DA) are presented, whereas in the non-DEJ-based evaluation section, tasks such as error classification and postediting are detailed, with definitions and guidelines, thus rendering this article a useful guide for evaluation projects. Following the detailed presentation of the previously mentioned metrics, the specificities of NMT are set forth along with suggestions for its evaluation, according to the latest studies. As human translators are the most adequate judges of the quality of a translation, emphasis is placed on the human metrics seen from a translator-judge perspective to provide useful methodology tools for interdisciplinary research groups that evaluate MT systems.

This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our ongoing mission of increasing language coverage and translation quality, and also describe work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.

Nowadays, hardly anyone working in the field of foreign language teaching and learning can imagine life without machine translation (MT) tools. Thanks to the rapid development of artificial intelligence, MT now most widely assumes a new form, the so-called Neural Machine Translation (NMT), which offers the potential for a wide application in foreign language learning (FLL). Therefore, the purpose of this review study is to explore different approaches to the efficient implementation of NMT into FLL and provide specific pedagogical implications for best practices. The PRISMA methodology for systematic reviews and meta-analyses was strictly followed. The search was conducted in two well-established databases, specifically Scopus and Web of Science, to generate sufficient data from research articles for further analysis. The findings of this systematic review indicate that NMT is an efficient tool for developing both productive (speaking and writing) and receptive (reading and listening) language skills, including mediation skills, which are relevant for translation. Moreover, the results show that NMT tools are especially suitable for advanced learners of L2, whose higher proficiency level enables them to critically reflect on the output of NMT texts more than beginners or lower-intermediate learners. Thus, the findings of this review study reveal that NMT has valuable implications for L2 pedagogy since it can serve as a very powerful online reference tool for FLL provided that teachers introduce students to its benefits but also limitations by implementing various teaching approaches.

Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another. Recently, Transformer-based neural machine translation (NMT) has achieved great break-throughs and has become a new mainstream method in both methodology and applications. In this article, we conduct an overview of Transformer-based NMT and its extension to other tasks. Specifically, we first introduce the framework of Transformer, discuss the main challenges in NMT and list the representative methods for each challenge. Then, the public resources and toolkits in NMT are listed. Meanwhile, the extensions of Transformer in other tasks, including the other natural language processing tasks, computer vision tasks, audio tasks and multi-modal tasks, are briefly presented. Finally, possible future research directions are suggested.

Machine translation (namely MT) has been one of the most popular fields in computational linguistics and Artificial Intelligence (AI). As one of the most promising approaches, MT can potentially break the language barrier of people from all over the world. Despite a number of studies in MT, there are few studies in summarizing and comparing MT methods. To this end, in this paper, we principally focus on presenting the two mainstream MT schemes: statistical machine translation (SMT) and neural machine translation (NMT), including their basic rationales and developments. Meanwhile, the detailed translation models are also presented, such as the word-based model, syntax-based model, and phrase-based model in statistical machine translation. Similarly, approaches in NMT, such as the recurrent neural network-based, attention mechanism-based, and transformer-based models are presented. Last but not least, the evaluation approaches also play an important role in helping developers to improve their methods better in MT. The prevailing machine translation evaluation methodologies are also presented in this article.

Context-aware neural machine translation (NMT), which targets at translating sentences with contextual information, has attracted much attention recently. A key problem for context-aware NMT is to effectively encode and aggregate the contextual information. BERT (Devlin et al., in: NAACL, 2019) has been proven to be an effective feature extractor in natural language understanding tasks, but it has not been well studied in context-aware NMT. In this work, we conduct a study about leveraging BERT to encode the contextual information for NMT, and explore three commonly used methods to aggregate the contextual features. We conduct experiments on five translation tasks and find that concatenating all contextual sequences as a longer one and then encoding it by BERT obtains the best translation results. Specifically, we achieved state-of-the-art BLEU scores on several widely investigated tasks, including IWSLT’14 German→English, News Commentary v11 English→German translation and OpenSubtitle English→Russian translation.