Introduction to the special issue on deep learning approaches for machine translation

Corpus-based machine translation (MT) has been the main approach to developing and implementing MT systems in both academia and the industry over the last three decades. In this field, the type and size of the corpus used for training MT engines have presented problems for both statistical MT (SMT) systems as well as neural MT (NMT) systems, being the two dominant corpusbased approaches. Moreover, language pairs such as Turkish-English have been understudied within this framework. This article aims to evaluate the translation quality in Turkish-to-English custom MT systems that have been trained on different corpus sizes and types. Two NMT engines and two SMT engines were trained on the KantanMT platform using two different training corpus types with either only domain-specific cardiology corpus or this corpus plus a mixed-domain corpus. The study conducted both automatic evaluations with metrics including BLEU, F-Measure and TER, as well as a comprehensive human evaluation with metrics including fluency, A/B test, and adequacy. Lastly, the study realized a separate, subjective terminology evaluation in order to investigate how differently MT systems handle terminology, as this is a crucial aspect for specific-domain text types such as cardiology. While the automatic evaluation results suggest the SMT engines to perform better than NMT engines, all human evaluators rated the mixed-domain NMT engine as the highest performing one. However, the terminology evaluation task demonstrated SMT to still be able to perform better and to commit less terminology errors, despite the industry and academia shifting toward NMT engines.

With the active participation of users in product review platforms, online consumer-generated content, and, more specifically, user-generated reviews, have become a clear reference in purchasing decision-making processes, which sometimes exceed the impact of advertising campaigns. A common feature of most tourism review platforms is the use of machine translation (MT) systems to immediately make reviews available to users in various languages. However, the quality of the MT output of these reviews varies greatly, primarily due to the subjective and unstructured nature of this digital genre. Different studies confirm that there are no universal quality rating scales. The assessment of MT output quality usually depends on factors such as the purpose of the text or the value given to the immediacy of the translation. New neural MT systems have been a revolution in the quality increase of the translated output; however, new lines of research are opening up to verify whether the quality of this new paradigm of MT can be assessed with the existing scales, mainly from previous rule-based systems and statistical translation, or whether it is necessary to develop new quality metrics specifically for these new intelligent systems. On the other hand, one of the questions that remain to be resolved in this new context of neural MT is whether the use of large amounts of textual data in the training of these systems is as effective as the use of less data but of higher quality and better-adjusted to the specialty and type of text for which it is used. Based on the hypothesis that each genre requires specific quality rating scales, this work identifies the error patterns and textual characteristics of online user reviews from a corpus-based approach analysis that will contribute to adapting quality rating scales to this specific digital genre.

Machine translation aims to minimize the language barrier between people of different linguistic backgrounds. Machine translation is an automatic translation technique between pairs of different languages. Machine translation using neural network or neural machine translation came into the picture due to several limitation associated with its predecessors which are rule based and statistical based models. For large data sets with a rich range of vocabulary, the neural network machine translation system provides fair translation accuracy. We have observed that there remain very few machine translators dedicated to Indian languages, especially those spoken in the North-East area. So our paper mainly focuses on implementing a neural machine translation system for the English-Assamese language. In this paper we used five different neural machine translation models for English-Assamese language pair using LSTMs and GRUs, along with an attention layer. We have made a comparison analysis based on the performance results of these models. We used BLEU Score to calculate the accuracy of these five models, thus achieving a BLEU score of 34.168%, which was the highest among the five models.

This paper introduces current-day automated metrics for neural machine translation evaluation and discusses two ways of result evaluation, which include evaluation using specific automated metrics as well as evaluation by an expert translator. Neural machine translation systems were tested for analysis and evaluation purposes. Google Translate and DeepL Translate systems, which apply a neural network approach, are presented as candidates. The following metrics were considered for evaluation: METEOR as a traditional reference-based metric, COMET as a neural reference-based metric, and COMET-kiwi as a neural reference-free metric. It can be noted that even if these metrics are automated, there is a huge human impact in this automation. Even models with a neural network approach are trained on data provided by humans, as today it is impossible to get rid of reference translations or quality estimations made by experts. Metrics allow to understand and better investigate machine translation, its features and limitations, as well as to determine the direction of its development. As a part of the analysis, a piece of source text was selected for translation, translated into the target languages using selected neural machine translation systems to get candidate translations, and then a reference translation was specified for each of them. The results of the metrics evaluation were helpful for understanding how close machine translation is to human translation, and also helped to learn more about the current stage of machine translation systems development. Expert evaluation helped to understand how well such systems are doing in terms of translation performance.

Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjabi to English NMT system. A parallel corpus for Punjabi to English containing MWEs was developed and used to train the different models of NMT. Punjabi is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively.

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker. As a result, some sentences are translated incorrectly. Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this information into the translation process. Both fields are relatively new and previous work within them is limited. Moreover, there are no readily available robust evaluation metrics for them, which makes it difficult to develop better systems, as well as track global progress and compare different methods. This thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. Three main challenges are addressed: (1) incorporating extra-textual information directly into NMT systems; (2) improving the machine translation of cohesion devices; (3) reliable evaluation for PersNMT and DocNMT.

The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems. In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely, actor-critic , that includes a novel reward combination module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi–English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.

In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.

Machine Translation (MT) has recently been strengthened by the emergence of deep neural network techniques, which have brought the dream of language translation in community-based practices and clinical practices closer to reality. This chapter introduces the clinical practice of MT, especially in low resource languages. The following are some of the highlights of this chapter. How MT played a crucial role in good social reasons is focused on in this chapter. The chapter starts with the history of translation technologies that have played a vital role in various crisis and relief scenarios as the Haitian earthquake in 2010 and Translators without Borders (TWB), respectively. The Standby Task Force (SBTF) deployed the technology to tackle misinformation during Coronavirus Pandemic 2019. Next, the chapter explores the importance of the MT system, where it describes MT literacy as required across society to impact the healthcare of human beings. It describes the challenges of translation for clinical texts. Some of those are highlighted as (a) Domain: characteristics of clinical text differentiated from texts with other domains, (b) code mixed low-resource languages: noisy clinical data and lower quantity of data in the digital form available for computation, (c) lack of clinical domain corpora, and (d) very rich morphological structure: e.g., procedures (e.g., hepatico-cholangio-jejuno-stomy) and chemicals (e.g., Hydro-xy-nitro-di-hydro-thy-mine). Strategies or approaches to building MT on the clinical text can be categorized as: (1) Rule-based MT (early days), (2) Statistical MT (SMT, one of the successful methods in MT history), and (3) Neural MT (NMT, recent advancements of MT). Both SMT and NMT that work on same grounds by learning from text and are the primary focus of this chapter. L earning transparency for the patient's use of clinical data is an essential component. Much of today's healthcare data is unstructured text, such as clinical text, doctor's notes, nursing notes, lab reports, test reports, and more. A clinical practitioner is faced with a language barrier for communicating with a patient to interpret patient histories, examine a clinical diagnosis, or restate the recommended treatment method and follow-up to aid comprehension. The only way a medical practitioner encourages patients to ask questions or respond to queries is by directing them to input text with the help of MT. Finally, the chapter discusses contemporary techniques with Neural Machine Translation (NMT), namely multilingual, transfer learning on clinical data.

Sanskrit is one of the earliest native languages and is correctly described as "the gods' language" because of its wide use in Indian religious literature from the past. However, it is becoming less popular in modern India. Due in significant part to the need for more materials for translation both in and out of Sanskrit, it is no longer commonly utilized. This study explores the feasibility of using machine translation (MT) to provide a link between Sanskrit and, one of the earliest native languages, and its contemporary descendant Hindi. A study was conducted between existing modelling methodologies, notably Statistical machine translation (SMT), and the proposed novel deep learning-based Machine translation strategy using a manually created parallel corpus for the Sanskrit-Hindi language pair. While SMT creates interpretations by mapping phrases from the languages of the source and destination, statistical models, and bilingual text corpora for learning parameters, neural machine translation (NMT) frequently models entire phrases in a single integrated model, using a convolutional neural network to calculate the probability of a word sequence. The proposed NMT model is implemented using an encoder-decoder with an attention mechanism paradigm and the inclusion of gated recurrent units. Our approach involved development of a novel model for Sanskrit-Hindi machine translation using deep learning and the creation of parallel corpora for the Sanskrit-Hindi language pair. The proposed model is evaluated on automated and human-based metrics, and results show that our proposed deep learning-based model outperforms statistical modelling techniques on Moses, surpassing them both with a BLEU score of 53.8% compared to 34.56%. This article examines the undiscovered area of machine translation from Sanskrit to Hindi and discusses the main benefits and drawbacks of statistical and neural machine translation while providing a fresh viewpoint on the subject.

Deep neural networks (DNNs) have provably enhanced the state-of-the-art natural language process (NLP) with their capability of feature learning and representation. As one of the more challenging NLP tasks, neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better than NMT in translation adequacy and word coverage. It is therefore a promising direction to combine the advantages of both NMT and SMT. In this article, we propose a deep neural network--based system combination framework leveraging both minimum Bayes-risk decoding and multi-source NMT, which take as input the N-best outputs of NMT and SMT systems and produce the final translation. In particular, we apply the proposed model to both RNN and self-attention networks with different segmentation granularity. We verify our approach empirically through a series of experiments on resource-rich Chinese⇒English and low-resource English⇒Vietnamese translation tasks. Experimental results demonstrate the effectiveness and universality of our proposed approach, which significantly outperforms the conventional system combination methods and the best individual system output.

In this paper, we focus on developing a Neural Machine Translation (NMT) system on English-to-Traditional-Chinese translation for financial prospectuses of companies which seek listing on the Hong Kong Stock Exchange. To the best of our knowledge, this is the first work on NMT for this specific domain. We propose a domain-specific NMT system by introducing a domain flag to indicate the target-side domain. By training the NMT model on the data from both the IPO corpus and the general domain corpus, we can expand the vocabulary while capturing the common writing styles and sentence structures. Our experimental results show that the proposed NMT system can achieve a significant improvement on translating the IPO documents. More significantly, through a blind assessment by a translator expert, our system outperforms two mainstream commercial tools, the Google translator and SDL Trado for some IPO documents.

Assembling translations from multi-engine machine translation outputs

Most neural machine translation (NMT) systems rely on parallel data, comprising text in the source language and its corresponding translation in the target language. While it’s acknowledged that context enhances NMT models, this work proposes a novel approach by incorporating external context, specifically explanations of source text meanings, akin to how human translators leverage context for comprehension. The suggested methodology innovatively addresses the challenge of incorporating lengthy contextual information into NMT systems. By employing state-of-the-art transformer-based models, external context is integrated, thereby enriching the translation process. A key aspect of the approach lies in the utilization of diverse text summarization techniques, strategically employed to efficiently distill extensive contextual details into the NMT framework. This novel solution not only overcomes the obstacle posed by lengthy context but also enhances the translation quality, marking a advancement in the field of NMT. Furthermore, the data-centric approach ensures robustness and effectiveness, yielding improvements in translation quality, as evidenced by a considerable boost in BLEU score points ranging from 0.46 to 1.87 over baseline models. Additionally, we make our dataset publicly available, facilitating further research in this domain.

Editing Taiwan divination Verses with controlled Language Strategies: Machine-Translation-Mediated Effective Communication Chung-ling Shih Abstract Aimed at fostering machine-translation-mediated communication across languages and cultures, this paper proposes editing Taiwan divination verses from the natural into controlled language to improve the comprehensibility of machine translation (MT) outputs. After editing 160 divination verses, and evaluating the semantic and grammatical accuracy of their English MTs that are produced by online Google Translate (a neural MT system for free), the author has identified several controlled language strategies. The lexical strategies include replacement of archaic Chinese words with vernacular Chinese ones, paraphrasing of culture references and insertion of explanations for metaphors. Grammatical strategies are the use of articles, determiners and possessive cases, and syntactical ones include the addition of conjunctions and the restoration of missing subjects or/and objects. The MT outputs of edited and unedited divination verses are compared. The findings show that the English MT outputs of edited texts have greatly improved their semantic, grammatical and syntactic accuracy, so the effectiveness of controlled language strategies is justified. Due to the effectiveness of editing verses with controlled language strategies, the goal of MT-enabled web-based communication across cultures is achieved. The practical significance is also discussed including culture acquisition and cost reduction. Full Text: PDF DOI: 10.15640/jflcc.v8n2a2