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With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.

This volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures. The book is of interest primarily to MT specialists, but also – in the wider fields of Computational Linguistics, Machine Learning and Data Mining – to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.

International audience; Most of the freely available parallel data to train the translation model of a statistical machine translation system comes from very specific sources (European parliament, United Nations, etc). Therefore, there is increasing interest in methods to perform an adaptation of the translation model. A popular approach is based on unsupervised training, also called self-enhancing. Both only use monolingual data to adapt the translation model. In this paper we extend the previous work and provide new insight in the existing methods. We report results on the translation between French and English. Improvements of up to 0.5 BLEU were observed with respect to a very competitive baseline trained on more than 280M words of human translated parallel data.

International audience; Most of statistical machine translation systems are combinations of various models, and tuning of the scaling factors is an important step. However, this optimisation problem is hard because the objective function has many local minima and the available algorithms cannot achieve a global optimum. Consequently, optimisations starting from different initial settings can converge to fairly different solutions. We present tuning experiments with the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, and compare them to tuning with the widely used downhill simplex method. With IWSLT 2006 Chinese-English data, both methods showed similar performance, but SPSA was more robust to the choice of initial settings.

International audience; This paper presents a strategy for detecting and using multi-word expressions in Statistical Machine Translation. Performance of the proposed strategy is evaluated in terms of alignment quality as well as translation accuracy. Evaluations are performed by using the Verbmobil corpus. Results from translation tasks from English-to-Spanish and from Spanish-to-English are presented and discussed.

International audience; This paper describes the development of French–English and English–French statistical machine translation systems for the 2012 WMT shared task evaluation. We developed phrase-based systems based on the Moses de-coder, trained on the provided data only. Additionally , new features this year included improved language and translation model adaptation using the cross-entropy score for the corpus selection.

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In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. In this paper we compare alignments tuned directly according to alignment F-score and BLEU score in order to investigate the alignment characteristics that are helpful in translation. We report results for two different SMT systems (a phrase-based and an n-gram-based system) on Chinese to English IWSLT data, and Spanish to English European Parliament data. We give alignment hints to improve BLEU score, depending on the SMT system used and the type of corpus.

The notion of word alignment of a bilingual parallel corpus is defined and the difficulties of the task are highlighted. Alignment evaluation procedures are described. First, straightforward, baseline methods are presented. Then state-of-the-art systems are described and qualitatively or quantitatively compared.

This paper presents a simple way of producing symmetric, phrase-based alignments, combining two single-word based alignments. Our algorithm exploits the asymmetries in the superposition of the two word alignments to detect the phrases that must be aligned as a whole. It was run with baseline word alignments produced by the Giza++ software and improved these alignments. The ability to treat some groups of words as a whole is essential in applications like machine translation. The paper also addresses the difficulty of the alignment evaluation task.

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.

En esta comunicación se presenta un sistema de traducción estocástica basado en el modelado mediante N-gramas de la probabilidad conjunta de textos bilingües. La unidad básica del modelo es la tupla, par de cadenas de palabras del lenguaje fuente (a traducir) y el lenguaje destino (traducción). La traducción se lleva a cabo mediante la maximización de una combinación lineal de los logaritmos de la probabilidad asignada a la traducción por el modelo de traducción y otras características, siguiendo la aproximación de entropía máxima. Las prestaciones del sistema de traducción son evaluadas con una tarea de traducción del habla: la traducción entre inglés y español (y viceversa) de transcripciones de intervenciones de los miembros del Parlamento Europeo. Los resultados alcanzados se encuentran al nivel del estado del arte. This communication introduces a stochastic machine translation system based on Ngram modelling of the joint probability of bilingual texts. The basic unit of this model is called a tuple and consists of a pair of both source (to be translated) language and target language (translation) word-strings. Translation is driven by a log-linear combination of the N-gram model probability and other features, according to the maximum entropy language modelling approach. The translation performance is evaluated by means of a speech-to-speech translation tasks: translation from Spanish to English (and viceversa) of European Parliament speeches. The system reaches a state-of-art performance. Este trabajo ha sido financiado parcialmente por la CICYT a través del proyecto TIC2002-04447-C02 (ALIADO) y la Unión Europea mediante el proyecto FP6-506738 (TC-STAR).

The Lingua-AlignmentSet distribution is a Perl Tools Library (and command-line utilities) to handle an Alignment Set, i.e. a set of sentences aligned at the word (or phrase) level. It provides methods to display the links, to apply a function to each alignment of the set, to evaluate the alignments against a reference, and more. One of the objectives of the module is to allow the user to perform all these operations without bothering with the particular physical format of the Alignment Set. Anyway it also provides format conversion methods.