565 research outputs found
Entrevista con Mikel Forcada
No full textMikel Forcada és Catedràtic d’Universitat al Departament de llenguatges i sistemes informàtics de la Universitat d’Alacant, on desenvolupa la seva investigació en el marc del grup de recerca Tansducens. La seva recerca està marcada pel seu interès en relació amb la traducció automàtica, la traducció assistida i el processament del llenguatge natural. Tothom coneix el sistema de traducció automàtica Apertium, però també han desenvolupat altres aplicacions com Bitextor, TagAlaginer, DocTrans, per citar-ne algunes, totes elles de codi obert. Mikel Forcada és president de l’Associació Europea de Traducció Automàtica (European Association of Machine Translation, EAMT). Destacarem a més que Mikel ha estat sempre predisposat a col·laborar i intercanviar punts de vista entre el món de la informàtica i el dels lingüistes i traductors, i aquesta entrevista és també fruit d’aquesta col·laboració. Hem preguntat a Mikel sobre el present i el futur de la traducció automàtica i els seus diferents desenvolupaments, y sobre el paper de traductors i lingüistes en aquesta evolució.Mikel Forcada is a full professor in the Department of Languages and Computing Systems at the University of Alicante. His work is heavily influenced by his interest in machine translation, computer assisted translation and processing natural language and carried out within the context of the research group Transducens. Their most well-known machine translation application is Apertium, but they have developed others such as Bitextor, TagAligner, DocTrans, all of which are written in open code.Forcada is the President of the European Association of Machine Translation, EAMT and has always been open to collaborating and exchanging points of view with those in the fields of computing, linguistics and translation. This interview is likewise the fruit of such collaboration, in which we quiz him about present and future trends in machine translation, its stages of development and the role of translators and linguists in its evolution.Mikel Forcada es Catedrático de Universidad en el Departamento de Lenguajes y sistemas informáticos de la Universidad de Alicante, donde desarrolla su investigación en el marco del grupo de investigación Transducens. Su investigación está marcada por su interés en relación con la traducción automática, la traducción asistida y el procesamiento del lenguaje natural. De todos conocido es el sistema de traducción automática Apertium, pero también han desarrollado otras aplicaciones como Bitextor, TagAligner, DocTrans, por nombrar algunas, todas ellas de código abierto. Mikel Forcada es presidente de la Asociación Europea de Traducción Automática (European Association of Machine Translation, EAMT). Destacaremos además que Mikel ha estado siempre predispuesto a colaborar e intercambiar puntos de vista entre el mundo de la informática y el de los lingüistas y traductores, y esta entrevista es también fruto de esta colaboración. Le hemos preguntado a Mikel sobre el presente y futuro de la traducción automática y sus distintos desarrollos, y sobre el papel de traductores y lingüistas en esta evolución
Free/Open-Source Machine Translation for the Low-Resource Languages of Spain (Invited Talk)
No full textWhile machine translation has historically been rule-based, that is, based on dictionaries and rules written by experts, most present-day machine translation is corpus-based. In the last few years, statistical machine translation, the dominant corpus-based approach, has been displaced by neural machine translation in most applications, in view of the better results reported, particularly for languages with very different syntax. But both statistical and neural machine translation need to be trained on large amounts of parallel data, that is, sentences in one language carefully paired with their translations in their other language, and this is a resource that may not be available for some low-resource languages. While some of the languages of Spain may be considered to be reasonably endowed with parallel corpora connecting them to Spanish or even to English - Basque, Catalan, Galician -, and are well-served with machine translation systems, there are many other languages which cannot afford them such as Aranese Occitan, Aragonese, or Asturian/Leonese. Fortunately, languages in this last group belong to the Romance language family, as Spanish does, and this makes translation from and into Spanish under a rule-based paradigm the only feasible approach. After describing briefly the main machine translation paradigms, I will describe the Apertium free/open-source rule-based machine translation platform, which has been used to build machine translation systems for these low-resource languages of Spain, indeed, sometimes the only ones available. The free/open-source setting has made linguistic data for these languages available for anyone in their linguistic communities to build other linguistic technologies for these low-resourced languages. For example, the Apertium family of bilingual and monolingual data has been converted into RDF and they have been made accessible on the Web as linked data
Entrevista con Mikel Forcada : presidente de la European Associaton of Machine Translation
No full textMikel Forcada es Catedrático de Universidad en el Departamento de Lenguajes y sistemas informáticos de la Universidad de Alicante, donde desarrolla su investigación en el marco del grupo de investigación Transducens. Su investigación está marcada por su interés en relación con la traducción automática, la traducción asistida y el procesamiento del lenguaje natural. De todos conocido es el sistema de traducción automática Apertium, pero también han desarrollado otras aplicaciones como Bitextor, TagAligner, DocTrans, por nombrar algunas, todas ellas de código abierto. Mikel Forcada es presidente de la Asociación Europea de Traducción Automática (European Association of Machine Translation, EAMT). Destacaremos además que Mikel ha estado siempre predispuesto a colaborar e intercambiar puntos de vista entre el mundo de la informática y el de los lingüistas y traductores, y esta entrevista es también fruto de esta colaboración. Le hemos preguntado a Mikel sobre el presente y futuro de la traducción automática y sus distintos desarrollos, y sobre el papel de traductores y lingüistas en esta evolución.Mikel Forcada és Catedràtic d'Universitat al Departament de llenguatges i sistemes informàtics de la Universitat d'Alacant, on desenvolupa la seva investigació en el marc del grup de recerca Tansducens. La seva recerca està marcada pel seu interès en relació amb la traducció automàtica, la traducció assistida i el processament del llenguatge natural. Tothom coneix el sistema de traducció automàtica Apertium, però també han desenvolupat altres aplicacions com Bitextor, TagAlaginer, DocTrans, per citar-ne algunes, totes elles de codi obert. Mikel Forcada és president de l'Associació Europea de Traducció Automàtica (European Association of Machine Translation, EAMT). Destacarem a més que Mikel ha estat sempre predisposat a col·laborar i intercanviar punts de vista entre el món de la informàtica i el dels lingüistes i traductors, i aquesta entrevista és també fruit d'aquesta col·laboració. Hem preguntat a Mikel sobre el present i el futur de la traducció automàtica i els seus diferents desenvolupaments, y sobre el paper de traductors i lingüistes en aquesta evolució.Mikel Forcada is a full professor in the Department of Languages and Computing Systems at the University of Alicante. His work is heavily influenced by his interest in machine translation, computer assisted translation and processing natural language and carried out within the context of the research group Transducens. Their most well-known machine translation application is Apertium, but they have developed others such as Bitextor, TagAligner, DocTrans, all of which are written in open code.Forcada is the President of the European Association of Machine Translation, EAMT and has always been open to collaborating and exchanging points of view with those in the fields of computing, linguistics and translation. This interview is likewise the fruit of such collaboration, in which we quiz him about present and future trends in machine translation, its stages of development and the role of translators and linguists in its evolution
Entrevista con Mikel Forcada : presidente de la European Associaton of Machine Translation
No full textMikel Forcada es Catedrático de Universidad en el Departamento de Lenguajes y sistemas informáticos de la Universidad de Alicante, donde desarrolla su investigación en el marco del grupo de investigación Transducens. Su investigación está marcada por su interés en relación con la traducción automática, la traducción asistida y el procesamiento del lenguaje natural. De todos conocido es el sistema de traducción automática Apertium, pero también han desarrollado otras aplicaciones como Bitextor, TagAligner, DocTrans, por nombrar algunas, todas ellas de código abierto. Mikel Forcada es presidente de la Asociación Europea de Traducción Automática (European Association of Machine Translation, EAMT). Destacaremos además que Mikel ha estado siempre predispuesto a colaborar e intercambiar puntos de vista entre el mundo de la informática y el de los lingüistas y traductores, y esta entrevista es también fruto de esta colaboración. Le hemos preguntado a Mikel sobre el presente y futuro de la traducción automática y sus distintos desarrollos, y sobre el papel de traductores y lingüistas en esta evolución.Mikel Forcada és Catedràtic d'Universitat al Departament de llenguatges i sistemes informàtics de la Universitat d'Alacant, on desenvolupa la seva investigació en el marc del grup de recerca Tansducens. La seva recerca està marcada pel seu interès en relació amb la traducció automàtica, la traducció assistida i el processament del llenguatge natural. Tothom coneix el sistema de traducció automàtica Apertium, però també han desenvolupat altres aplicacions com Bitextor, TagAlaginer, DocTrans, per citar-ne algunes, totes elles de codi obert. Mikel Forcada és president de l'Associació Europea de Traducció Automàtica (European Association of Machine Translation, EAMT). Destacarem a més que Mikel ha estat sempre predisposat a col·laborar i intercanviar punts de vista entre el món de la informàtica i el dels lingüistes i traductors, i aquesta entrevista és també fruit d'aquesta col·laboració. Hem preguntat a Mikel sobre el present i el futur de la traducció automàtica i els seus diferents desenvolupaments, y sobre el paper de traductors i lingüistes en aquesta evolució.Mikel Forcada is a full professor in the Department of Languages and Computing Systems at the University of Alicante. His work is heavily influenced by his interest in machine translation, computer assisted translation and processing natural language and carried out within the context of the research group Transducens. Their most well-known machine translation application is Apertium, but they have developed others such as Bitextor, TagAligner, DocTrans, all of which are written in open code.Forcada is the President of the European Association of Machine Translation, EAMT and has always been open to collaborating and exchanging points of view with those in the fields of computing, linguistics and translation. This interview is likewise the fruit of such collaboration, in which we quiz him about present and future trends in machine translation, its stages of development and the role of translators and linguists in its evolution
Editors’ foreword to the invited issue on SMT and NMT
No full textUntil quite recently, phrase-based statistical machine translation (PB-SMT) (Koehn et al. 2003, 2007; Koehn 2010) was indisputably the dominant paradigm in the field of MT. Papers suggesting how neural networks could be used for MT had been published twenty years ago (Chalmers 1990; Chrisman 1991; Castano and Casacuberta 1997; Forcada and Ñeco 1997; Ñeco and Forcada 1997), but the hardware around at the time was insufficient to support the amount of computation required for realistic experimentation
Experiments on domain adaptation for patent machine translation in the PLuTO project
No full textThe PLUTO1 project (Patent Language Translations Online) aims to provide a rapid solution for the online retrieval and translation of patent documents through the integration of a number of existing state-of-the-art components provided by the project partners. The paper presents some of the experiments on patent domain adaptation of the Machine Translation (MT) systems used in the PLuTO project. The experiments use the International Patent Classification for domain adaptation and are focused on the English–French language pair
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Balance and Respect vs. Commodification and Control: Conflicting Values in the Work of Maya-Tsotsil Author Mikel Ruiz
No full textBalance and Respect vs. Commodification and Control: Conflicting Values in the Work of Maya-Tsotsil Author Mikel Rui
Recurrent neural networks can learn simple, approximate regular languages
No full textA number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable of inferring deterministic finite automata from sets of example and counterexample strings; however, discrete algorithmic methods are much better at this task and clearly outperform DTRNN in terms of space and time complexity. We show how DTRNN may be used to learn not the exact language that explains the whole learning set but an approximate and much simpler language that explains a great majority of the examples by using simpler rules. This is accomplished by gradually varying the error function in such a way that the DTRNN is eventually allowed to classify clearly but incorrectly those strings that it has found to be difficult to learn, which are treated as exceptions. The results show that in this way, the DTRNN usually manages to learn a simplified approximate language
Hybrid rule-based - example-based MT: feeding apertium with sub-sentential translation units
No full textThis paper describes a hybrid machine translation (MT) approach that consists of integrating bilingual chunks (sub-sentential translation units) obtained from parallel corpora into an MT system built using the Apertium free/open-source rule-based machine translation platform, which uses a shallow-transfer translation approach. In the integration of bilingual chunks, special care has been
taken so as not to break the application of the existing Apertium structural transfer rules, since this would increase the number of ungrammatical translations. The method consists of (i) the application of a dynamic-programming algorithm to compute the best translation coverage of the input sentence given the collection of bilingual chunks available; (ii) the translation of the input sentence as usual by Apertium; and (iii) the application of a language model to choose one of the possible translations for each of the bilingual chunks detected. Results are reported for the translation from English-to-Spanish, and vice versa, when marker-based bilingual chunks automatically obtained from parallel
corpora are used
Meta-Evaluation of a Diagnostic Quality Metric for Machine Translation
No full textDiagnostic evaluation of machine translation
(MT) is an approach to evaluation that
provides finer-grained information compared
to state-of-the-art automatic metrics.
This paper evaluates DELiC4MT, a diagnostic
metric that assesses the performance
of MT systems on user-defined linguistic
phenomena. We present the results obtained
using this diagnostic metric when
evaluating three MT systems that translate
from English to French, with a comparison
against both human judgements and
a set of representative automatic evaluation
metrics. In addition, as the diagnostic
metric relies on word alignments, the
paper compares the margin of error in diagnostic
evaluation when using automatic
word alignments as opposed to gold standard
manual alignments. We observed that
this diagnostic metric is capable of accurately
reflecting translation quality, can be
used reliably with automatic word alignments
and, in general, correlates well with
automatic metrics and, more importantly,
with human judgements
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