Statistical Machine Translation Customization between Turkish and 11 Languages

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.

In the modern era, the field of Machine Translation (MT) has seen a significant shift towards Neural Machine Translation (NMT) techniques, which have surpassed traditional Statistical Machine Translation (SMT) models in terms of the quality of translation. Despite this, the efficacy of these techniques may differ based on the language combination in consideration. While SMT is somewhat more flexible in this regard, NMT often needs sizable parallel corpora to attain high translation accuracy. As a result, a benchmark system capable of offering sufficient translation for languages with limited resources, like Nepali, remains a pipe dream. This paper focuses on translating text using statistical and neural MT techniques for the under-resourced English-Nepali language pair. As a part of this system development, we built a parallel corpus of English-Nepali in the tourism domain. We explore the impact of different tokenization techniques on translation outcomes. A substantial analysis is also done for the performance of both approaches using automatic evaluation metrics, BLEU and TER. This paper aims to provide insights into the applicability of SMT and NMT for the under-resourced English-Nepali language pair in light of two popular epitomes of tokenization and to determine the most effective approach for achieving accurate translations.

Challenges for Arabic Machine TranslationAbdelhadi Soudi1, Ali Farghaly2, Günter Neumann3, and Rabih Zbib4(editors)(1École Nationale de l'Industrie Minérale, 2Monterey Institute of International Studies, 3German Research Center for Artificial Intelligence, 4BBN Technologies)John Benjamins Publishing Company (Natural language processing series, edited by Ruslan Mitkov, volume 9), 2012, viii+157 pp; hardbound, ISBN 978-90-272-4995-1, $135.00, €90.00As the name of the book suggests, Arabic is a rather challenging language to handle in a machine translation (MT) system. The main challenges that are discussed and tackled throughout the book include Arabic complex morphology and a different syntactic structure from English.The book assembles recent work targeting Arabic-to-English and English-to-Arabic machine translation. Paradigms for MT include statistical MT (SMT) and example-based MT (EBMT). Techniques to improve the MT quality include preprocessing (Arabic segmentation, reordering) and syntactic models for SMT, and generalized matching for EBMT. The domains of research that are presented in the book are rather broad, but it lacks a unified experimental environment, rendering a comparison of the empirical results hard. One additional caveat is the partial quantitative information about Arabic translation challenges. Statistics about Arabic ambiguity and about the amount and complexity of reorderings, and a comparison to other language pairs (e.g., German–English), could be meaningful and relate well to the title of the book. Additionally, this information can justify the need for a special treatment of the Arabic language within the MT community.Finally, the strong side of the book includes exploration of less dominant research fields as English-to-Arabic MT and EBMT. A comparison of SMT and EBMT, and a discussion of advantages and disadvantages of the paradigms is also given. I would recommend the book for MT professionals as a useful starting point containing references and established work on Arabic MT.—Saab Mansour, RWTH Aachen University, Aachen, GermanySaab Mansour is a research assistant and a Ph.D. student at RWTH Aachen University since 2008. Mansour's address is Chair of Computer Science 6, RWTH Aachen University, Aachen, Germany; e-mail: mansour@cs.rwth-aachen.de.

In this paper, we present our work on a case study between statistical machine translation (SMT) and rule-based machine translation (RBMT) systems on English-Indian language and Indian to Indian language perspective. Main objective of our study is to make a five-way performance comparison: such as, (a) SMT and RBMT; (b) SMT on English–Indian language; (c) RBMT on English–Indian language; (d) SMT on Indian to Indian language perspective; (e) RBMT on Indian to Indian language perspective. Through a detailed analysis, we describe the rule-based and the statistical machine translation system developments and its evaluations. Further, with a detailed error analysis, we point out the relative strengths and weaknesses of both the systems. The observations based on our study are: (a) SMT systems outperform RBMT; (b) In the case of SMT: English to Indian language MT systems perform better than Indian to English language MT systems; (c) In the case of RBMT: English to Indian language MT systems perform better than Indian to English language MT systems; (d) SMT systems perform better for Indian to Indian language MT systems compared to RBMT. Effectively, we shall see that even with a small amount of training corpus SMT system has many advantages for high-quality domain-specific machine translation over that of a rule-based counterpart.

This work investigates the potential use of post-edited machine translation (MT) outputs as reference translations for automatic machine translation evaluation, focusing mainly on the following important question: Is it necessary to take into account the machine translation system and the source language from which the given post-edits are generated? In order to explore this, we investigated the use of post-edits originating from different machine translation systems (two statistical systems and two rule-based systems), as well as the use of post-edits originating from two different source languages (English and German). The obtained results shown that for comparison of different systems using automatic evaluation metrics, a good option is to use a post-edit originating from a high-quality (possibly distinct) system. A better option is to use it together with other references and post-edits, however post-edits originating from poor translation systems should be avoided. For tuning or development of a particular system, post-edited output of this same system seems to be the best reference translation.

The background of machine translation dates back to the 1950s when scientists began exploring the use of computers for translation. Motivated by various challenges and needs, such as the high cost of manual translation and cross-cultural communication, machine translation has become a pivotal field. This overview delves into the research background, content, methods, key figures, conclusions, and future prospects of machine translation. It summarizes automatic evaluation metrics, corpus construction, and transfer learning, all of which contribute to enhancing translation performance. Currently, there are three mainstream categories of methods, which include rule-based translation, statistical translation, and neural network-based translation. The rule-based translation method relies on language rules and dictionaries for translation. Statistical machine translation involves the use of extensive bilingual corpora for identification and translation. The conclusion emphasizes the potential of neural machine translation, yet acknowledges challenges in diverse languages, low-resource languages, and specialized terminology.

In terms of language technology, Maithili is a resource-poor language. It is spoken in India and Nepal and is one of the 22 scheduled languages in India. Maithili has almost no language technology resource. English in India happens to be a dominant language in terms of content and usage. However, since more than 90% of Indians do not use English, a translation from English to Maithili becomes critical. An absence of basic tools in this language has affected resource creation of machine translation (MT). The present work discusses efforts for language technology resource (LTR) creation and divergence study for a statistical English-Maithili MT (EMMT) system. Creating any statistical MT (SMT) system requires sizeable parallel, aligned corpora for training and testing. Creating general-purpose source corpora for English language and creating translation equivalents with possible alignments require time and effort. The paper focuses on the data collection methods, cleaning, the size and structure of the text corpora, alignment and parallelization strategies, training, testing, and a study of divergence between the language pair.

Applying convolutional attention mechanisms and Human Memory Search for effective English-Urdu translation

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches for the translation of local dialects by exploiting linguistic information of the standard language. The method iteratively learns multiple segmentation schemes that are consistent with (1) the standard dialect segmentations and (2) the phrasal segmentations of an SMT system trained on the resegmented bitext of the local dialect. In a second step multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating three Japanese local dialects (Kumamoto, Kyoto, Osaka) into three Indo-European languages (English, German, Russian) revealed that the proposed system outperforms SMT engines trained on character-based as well as standard dialect segmentation schemes for the majority of the investigated translation tasks and automatic evaluation metrics.KeywordsMachine TranslationTarget LanguageSource LanguageStatistical Machine TranslationWord SegmentationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

We conduct the first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators. Our case study is Warbreaker, a popular fantasy novel originally written in English, which we translate into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences) with two data-driven approaches to Machine Translation (MT): phrase-based statistical MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained on over 100 million words of fiction. In the post-editing experiment, six professional translators with previous experience in literary translation translate subsets of this chapter under three alternating conditions: from scratch (the norm in the novel translation industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the time taken to translate each sentence, as well as the number of pauses and their duration. Based on these measurements, and using mixed-effects models, we study post-editing effort across its three commonly studied dimensions: temporal, technical and cognitive. We observe that both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort, post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but longer pauses (14 and 25%).

In present Statistical Machine Translation (SMT) systems, alignment is trained in a previous stage as the translation model. Consequently, alignment model parameters are not tuned in function of the translation task, but only indirectly. In this paper, we propose a novel framework for discriminative training of alignment models with automated translation metrics as maximization criterion. In this approach, alignments are optimized for the translation task. In addition, no link labels at the word level are needed. This framework is evaluated in terms of automatic translation evaluation metrics, and an improvement of translation quality is observed.

Automatically assessing the quality of machine translation is an important yet challenging task for machine translation research. Translation quality assessment is understood as predicting translation quality without reference to the source text. Translation quality depends on the specific machine translation system and often requires post-editing. Manual editing is a long and expensive process. Since the need to quickly determine the quality of translation increases, its automation is required. In this paper, we propose a quality assessment method based on ensemble supervised machine learning methods. The bilingual corpus WMT 2019 for the EnglishRussian language pair was used as data. The text data volume is 17089 sentences, 85% of the data was used for training, and 15% for testing the model. Linguistic functions extracted from the text in the source and target languages were used as features for training the system, since it is these characteristics that can most accurately characterize the translation in terms of quality. The following tools were used for feature extraction: a free language modeling tool based on SRILM and a Stanford POS Tagger parts of speech tagger. Before training the system, the text was preprocessed. The model was trained using three regression methods: Bagging, Extra Tree, and Random Forest. The algorithms were implemented in the Python programming language using the Scikit learn library. The parameters of the random forest method have been optimized using a grid search. The performance of the model was assessed by the mean absolute error MAE and the root mean square error RMSE, as well as by the Pearsоn coefficient, which determines the correlation with human judgment. Testing was carried out using three machine translation systems: Google and Bing neural systems, Mouses statistical machine translation systems based on phrases and based on syntax. Based on the results of the work, the method of additional trees showed itself best. In addition, for all categories of indicators under consideration, the best results are achieved using the Google machine translation system. The developed method showed good results close to human judgment. The system can be used for further research in the task of assessing the quality of translation.

Machine translation (MT) has become increasingly important and popular in the past decade, leading to the development of MT evaluation metrics aiming at automatically assessing MT output. Most of these metrics use reference translations to compare systems output, therefore, they should not only detect MT errors but also be able to identify correct equivalent expressions so as not to penalize them when those are not displayed in the reference translations. With the aim of improving MT evaluation metrics a study has been carried out of a wide panorama of linguistic features and their implications. For that purpose a Spanish and an English corpora containing hypothesis and reference translations have been analysed from a linguistic point of view, so that common errors can be detected and positive equivalencies highlighted. This article focuses on this qualitative analysis describing the linguistic phenomena that should be considered when developing an automatic MT evaluation metric. The results of this analysis have been used to develop an automatic MT evaluation metric that takes into account different dimensions of language. A brief review of the metric and its evaluation are also provided.