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The aim of the paper is to find out whether it is necessary to use all automatic measures of error rate and accuracy when evaluating the quality of machine translation output from the synthetic Slovak language into the analytical English language. We used multiple comparisons for the analysis and visualized the results for each sentence through the icon graphs. Based on the results, we can state that all examined metrics, which are based on textual similarity, except the f-measure, are needed to be included in the MT quality evaluation when analyzing, sentence by sentence, the machine translation output.

Recent advances in end-to-end neural machine translation models have achieved promising results on high-resource language pairs such as En -> Fr and En -> De. One of the major factor behind these successes is the availability of high quality parallel corpora. We explore two strategies on leveraging abundant amount of monolingual data for neural machine translation. We observe improvements by both combining scores from neural language model trained only on target monolingual data with neural machine translation model and fusing hidden-states of these two models. We obtain up to 2 BLEU improvement over hierarchical and phrase-based baseline on low-resource language pair, Turkish -> English. Our method was initially motivated towards tasks with less parallel data, but we also show that it extends to high resource languages such as Cs -> En and De -> En translation tasks, where we obtain 0.39 and 0.47 BLEU improvements over the neural machine translation baselines, respectively. (C) 2017 Elsevier Ltd. All rights reserved.

This paper presents a methodology to address lexical disambiguation in a standard phrase-based statistical machine translation system. Similarity among source contexts is used to select appropriate translation units. The information is introduced as a novel feature of the phrase-based model and it is used to select the translation units extracted from the training sentence more similar to the sentence to translate. The similarity is computed through a deep autoencoder representation, which allows to obtain effective lowdimensional embedding of data and statistically significant BLEU score improvements on two different tasks (English-to-Spanish and English-to-Hindi). © 2016 Elsevier B.V. All rights reserved., The work of the first author has been supported by FPI UPV pre-doctoral grant (num. registro - 3505). The work of the second author has been supported by Spanish Ministerio de Economia y Competitividad, contract TEC2015-69266-P and the Seventh Framework Program of the European Commission through the International Outgoing Fellowship Marie Curie Action (IMTraP-2011-29951). The work of the third author has been supported by the Spanish Ministerio de Economia y Competitividad, SomEMBED TIN2015-71147-C2-1-P research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030).

This paper presents the machine translation system for English to Hindi which is based on the concept of machine learning of semantically correct corpus. The machine learning process is based on quantum neural network (QNN), which is a novel approach to recognize and learn the corpus pattern in a realistic way. It presents the structure of the system, machine translation system and the performance results. System performs the task of translation using its knowledge gained during learning by inputting pair of sentences from source to target language i.e. English and Hindi. Like a person, the system also acquires the necessary knowledge required for translation in implicit form by inputting pair of sentences. The effectiveness of the proposed approach has been analyzed by using 4600 sentences of news items from various newspapers and from Brown Cuprous. During simulations and evaluation, BLEU score achieved 0.9814 accuracy, NIST score achieved 7.3521, ROUGE-L score achieved 0.9887, METEOR score achieved 0.7254 and human based evaluation achieved 98.261%. The proposed system achieved significantly higher accuracy than AnglaMT, Anuvadaksh, Bing and Google Translation.

This paper contributes an empirical study of the application of five state-of-the-art machine translation to the trans- lation of low-resource languages. The methods studied were phrase-based, hierarchical phrase-based, the operational sequence model, string-to-tree, tree-to-string statistical machine translation methods between English (en) and the under resourced languages Lao (la), Myanmar (mm), Thai (th) in both directions. The performance of the machine translation systems was automatically measured in terms of BLEU and RIBES for all experiments. Our main findings were that the phrase-based SMT method generally gave the highest BLEU scores. This was counter to expectations, and we believe indicates that this method may be more robust to limitations on the data set size. However, when evaluated with RIBES, the best scores came from methods other than phrase-based SMT, indicating that the other methods were able to handle the word re-ordering better even under the constraint of limited data. Our study achieved the highest reported results on the data sets for all translation language pairs.

Statistical Machine Translation (SMT) is one of the most popular methods for machine translation. In this work, we carried out English-Malay SMT by acquiring an English-Malay parallel corpus in computer science domain. On the other hand, the training parallel corpus is from a general domain. Thus, there will be a lot of out of vocabulary during translation. We attempt to improve the English-Malay SMT in computer science domain using a dictionary and an English lemmatizer. Our study shows that a combination of approach using bilingual dictionary and English lemmatization improves the BLEU score for English to Malay translation from 12.90 to 15.41.

HighlightsWe study how translation affects ESL vocabulary learning.A reading tutor using language technologies measures each student's learning.Use of translation increases the retrieval time of L2 lexical items.Excessive use of translation decreases long-term retention of L2 lexical items. This article assesses the impact of translation on the acquisition of vocabulary for higher-intermediate level students of English for Speakers of Other Languages (ESOL). The use of translation is a relevant issue in the research of Second Language (L2) acquisition and different authors provide arguments on both sides of the issue. Language technologies serve this issue in both the usability of automatic translation and the automatic detection of lexical phenomena. This paper will explore translation as it affects the acquisition of new words in context when students are given real texts retrieved from the Internet in a web-based interface. The students can instantly obtain the dictionary definition of a word and its translation to their native language. This platform can accurately measure how much each student relies on translation and compare this to their accuracy and fluency on a lexical retrieval task using words seen in the texts. Results show that abundant use of translation may increase accuracy in the short term, but in the longer term, it negatively affects accuracy and possibly fluency. However, students who use translation in moderation seem to benefit the most in this lexical task. This paper provides a focused and precise way to measure the relevant variables for each individual student, and new findings that contribute to our understanding of the impact of the use of translation in language learning.

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. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments., Comment: machine translation, statistical machine translation, neural machine trasnlation, nlp, text processing, medical communication

This paper addresses the problem of statistical machine translation between highly inflected languages. Even when dealing with closely-related language pairs, statistical machine translation encounters problems if the parallel corpus is not big enough. To reduce the problem of data sparsity, we use the approach called factored translation, which has proven successful when translating between English and a morphologically rich language. We show that it is even more useful when translating between two highly inflected languages. The main contribution of the paper involves two extensions of the factored translation approach. First, we propose a new, more general asynchronous framework for training translation components, where lemmas in the lemma component and MSD tags in the MSD component are aligned independently of alignment done for surface word forms. The second contribution of the paper is a new technique for efficient use of a bilingual dictionary in the translation process. A dictionary is introduced into the lemma component to improve lexical translation. Dictionary use is based on entropy. We tested our enhanced translation approach on the Slovenian-Serbian language pair. The system was trained on a freely available OpenSubtitle corpus. The results show improvements in automatic scores (BLEU and TER). The approach could be used for other language pairs, especially if one or both are highly inflected.

This is the author’s version of a work that was accepted for publication in Computer Speech and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech and Language, 28, 3 (2015) DOI: 10.1016/j.csl.2013.10.003, One of the aims of Assistive Technologies is to help people with disabilities to communicate with others and to provide means of access to information. As an aid to Deaf people, we present in this work a production-quality rule-based machine system for translating from Spanish to Spanish Sign Language (LSE) glosses, which is a necessary precursor to building a full machine translation system that eventually produces animation output. The system implements a transfer-based architecture from the syntactic functions of dependency analyses. A sketch of LSE is also presented. Several topics regarding translation to sign languages are addressed: the lexical gap, the bootstrapping of a bilingual lexicon, the generation of word order for topic-oriented languages, and the treatment of classifier predicates and classifier names. The system has been evaluated with an open-domain testbed, reporting a 0.30 BLEU (BiLingual Evaluation Understudy) and 42% TER (Translation Error Rate). These results show consistent improvements over a statistical machine translation baseline, and some improvements over the same system preserving the word order in the source sentence. Finally, the linguistic analysis of errors has identified some differences due to a certain degree of structural variation in LSE.

This article provides a guide for readers interested in deepening their knowledge of the strengths and weaknesses of the different machine translation (MT) quality metrics, and presents a methodology and tooling developed respectively by LexisNexis ® and its MT provider Asia Online™ as part of a human quality assessment framework for patent translation. The methodology is designed specifically to compensate for the shortcomings of automated evaluation.

Concept classification has been used as a translation method and has shown notable benefits within the suite of speech-to-speech translation applications. However, the main bottleneck in achieving an acceptable performance with such classifiers is the cumbersome task of annotating large amounts of training data. Any attempt to develop a method to assist in, or to completely automate, data annotation needs a distance measure to compare sentences based on the concept they convey. Here, we introduce a new method of sentence comparison that is motivated from the translation point of view. In this method the imperfect translations produced by a phrase-based statistical machine translation system are used to compare the concepts of the source sentences. Three clustering methods are adapted to support the concept-base distance. These methods are applied to prepare groups of paraphrases and use them as training sets in concept classification tasks. The statistical machine translation is also used to enhance the training data for the classifier which is crucial when such data are sparse. Experiments show the effectiveness of the proposed methods.

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general.

In this paper, an effort is made to offer some approaches and solutions to remove the problems in the softwares of English to Persian translations through their comprehensive analysis. Machine translation is the automatic transfer of message from one language to another. Considering the irregularities and problems in translating texts from English to Persian caused by available softwares of machine translations, here, we are going to show that through the introduction of an appropriate framework we are able to remove the problems of the English translation weaknesses. The theoretical backgrounds and review literature are discussed in details. The linguistic aspects of machine translation, the available systems, the problems, translation approaches and evaluation of machine translation systems are also discussed. The machine translation softwares of Padideh, Pars and Google as well as bilingual dictionary of Hezareh are introduced. Data are collected and based on the obtained results we can conclude that through an analysis of lexical networks and analytic systems, we are able reduce the current problems and enhance the quality of machine translation.

A brief review of developments in using machine translation and/or machine assisted/aided translation forms a backdrop for an exploration of its applications in the processing of patent information. Particular reference is made to the message threads on this topic on the `patent information users group-Listserver' (PIUG-L), and to developments in this area by Derwent Information and other commercial organisations. The main language pairs referred to are Japanese into/from English and German into/from English. Some details about `free' web-based translation systems are also included.