GREAT: open source software for statistical machine translation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-011-9097-6[EN] In this article, the first public release of GREAT as an open-source, statistical machine translation (SMT) software toolkit is described. GREAT is based on a bilingual language modelling approach for SMT, which is so far implemented for n-gram models based on the framework of stochastic finite-state transducers. The use of finite-state models is motivated by their simplicity, their versatility, and the fact that they present a lower computational cost, if compared with other more expressive models. Moreover, if translation is assumed to be a subsequential process, finite-state models are enough for modelling the existing relations between a source and a target language. GREAT includes some characteristics usually present in state-of-the-art SMT, such as phrase-based translation models or a log-linear framework for local features. Experimental results on a well-known corpus such as Europarl are reported in order to validate this software. A competitive translation quality is achieved, yet using both a lower number of model parameters and a lower response time than the widely-used, state-of-the-art SMT system Moses. © 2011 Springer Science+Business Media B.V.Study was supported by the EC (FEDER, FSE), the Spanish government (MICINN, MITyC, “Plan E”, under Grants MIPRCV “Consolider Ingenio 2010”, iTrans2 TIN2009-14511, and erudito.com TSI-020110-2009-439), and the Generalitat Valenciana (Grant Prometeo/2009/014).González Mollá, J.; Casacuberta Nolla, F. (2011). GREAT: open source software for statistical machine translation. Machine Translation. 25(2):145-160. https://doi.org/10.1007/s10590-011-9097-6S145160252Amengual JC, Benedí JM, Casacuberta F, Castaño MA, Castellanos A, Jiménez VM, Llorens D, Marzal A, Pastor M, Prat F, Vidal E, Vilar JM (2000) The EUTRANS-I speech translation system. 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New Confidence Measures for Statistical Machine Translation

International audienceA confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1\% with an F-measure of 0.708

An Open Source Toolkit for Word-level Confidence Estimation in Machine Translation

International audienceRecently, a growing need of Confidence Estimation (CE) for Statistical Machine Translation (SMT) systems in Computer Aided Translation (CAT), was observed. However, most of the CE toolkits are optimized for a single target language (mainly English) and, as far as we know, none of them are dedicated to this specific task and freely available. This paper presents an open-source toolkit for predicting the quality of words of a SMT output, whose novel contributions are (i) support for various target languages, (ii) handle a number of features of different types (system-based, lexical , syntactic and semantic). In addition, the toolkit also integrates a wide variety of Natural Language Processing or Machine Learning tools to pre-process data, extract features and estimate confidence at word-level. Features for Word-level Confidence Estimation (WCE) can be easily added / removed using a configuration file. We validate the toolkit by experimenting in the WCE evaluation framework of WMT shared task with two language pairs: French-English and English-Spanish. The toolkit is made available to the research community with ready-made scripts to launch full experiments on these language pairs, while achieving state-of-the-art and reproducible performances

Translating Articles in the Humanities and Social Sciences.

International audienceMy study mainly consists in analysing precise examples rather than developing long theories, as the readers of this journal are not translation experts, but would benefit from a translator's viewpoint for their own research. The study focuses on written articles and leaves oral communications aside because they entail different translation issues (e.g. ways to address an English-speaking audience, simpler syntax, or words easy to pronounce for a non-native English speaker). The corpus of the article is extracted from my own experience as a freelance translator and tackles different fields, i.e. management, law, psychology and even geography. All the examples are either translated from English to French or from French to English. My study aims to debunk misconceptions about translation, more specifically in academic environments. I chose to divide my presentation in six main stereotypes : 1) Translating is easier and quicker than writing. Wrong. Translating is a long-term process which involves, among other things, language skills, content-based knowledge, appropriating the rationale and ideas of the author, and rewriting the whole article, the translator becoming a co-author of the article. 2) Machine translation yields good results. Wrong. Even if machine translation is a useful, time-saving tool for translators and researchers, it cannot replace human translators who are still needed at least for post-editing. 3) Literal translation ensures the quality of the translation. Wrong. Being faithful to the words in the source language text would often sound awkward in the target language ; the translated article would sound like a translation, without intending to do so for stylistic reasons. A case in point is, when authors can translate their own articles, they rephrase their own work and usually choose a different perspective from their original article ; they do not convey exactly the same meaning in the translated article. 4) Everyone speaks the same English. Wrong. Each country-or even states for larger countries-use English differently and have developed their own English. Authors should therefore write their articles differently when they submit their paper to a British, American, Australian, Canadian or Indian journal. This factor also depends on the scientific community you belong to, as it will superimpose specific terminology, phraseology and standards to tackle the same subject

Dimensionality reduction methods for machine translation quality estimation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-013-9139-3[EN] Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets.This work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no. 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).González Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Dimensionality reduction methods for machine translation quality estimation. Machine Translation. 27(3-4):281-301. https://doi.org/10.1007/s10590-013-9139-3S281301273-4Amaldi E, Kann V (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci 209(1–2):237–260Anderson TW (1958) An introduction to multivariate statistical analysis. 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The Impact of AI from the Perspective of Multilingual Students

In a world marked by the rapid ascension of AI technology, the consequences of this transformation extend across industries. Automation is becoming more prevalent, and human-machine interactions are redefining job markets. This article delves into the influence of Artificial Intelligence (AI) on the professional landscape, with a focus on the experiences of multilingual students. As AI technologies reach their zenith, traditional, labor-intensive sectors are adapting to the reality of AI-induced unemployment. The shift is most apparent in fields dependent on manual labor, such as construction, manufacturing, and electronics assembly. Industries that have thrived on repetitive, labor-intensive tasks are now transitioning to AI, replacing human workers with automated machines that offer precision, efficiency, and reduced operational risks. Consequently, we are witnessing the onset of an AI-induced unemployment wave that has far-reaching implications. Parallel to the automation of manual labor, language-related sectors are undergoing substantial changes. Translation, simultaneous interpretation, foreign language instruction, and editorial work are no exceptions. The meteoric rise of machine translation, fueled by advances in AI, has reshaped this landscape. Machine translation, evolving from rudimentary word-for-word conversion to a context-aware understanding of language nuances, is poised to challenge human translators. However, its capabilities remain concentrated in widely used languages, and challenges arise when translating less common or non-universal languages. The predicament of translating non-universal languages underscores the limitations of AI, demonstrating that it can provide invaluable assistance but may not replace the finesse of human language experts. This shift leaves multilingual students and language professionals in a unique position. They can add value by leveraging their deep understanding of languages, cultures, and contextual nuances. The skill of bridging linguistic and cultural gaps is becoming increasingly indispensable in international business, diplomacy, and cross-cultural communication. As the AI era advances, multilingual individuals must recognize the unique skills they possess in understanding emotional, cultural, and contextual subtleties. In an environment where AI excels at translating factual content but falls short in conveying the emotions of a letter, the nuances of a poem, or the cultural depth of a phrase, multilingual students and language professionals stand as indispensable figures