Online Incremental Machine Translation

In this thesis we investigate the automatic improvements of statistical machine translation systems at runtime based on user feedback. We also propose a framework to use the proposed algorithms in large scale translation settings

Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

Knowledge-based machine translation (KBMT) techniques yield high quality in domains with detailed semantic models, limited vocabulary, and controlled input grammar. Scaling up along these dimensions means acquiring large knowledge resources. It also means behaving reasonably when definitive knowledge is not yet available. This paper describes how we can fill various KBMT knowledge gaps, often using robust statistical techniques. We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9

Online adaptation strategies for statistical machine translation in post-editing scenarios

[EN] One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adapta- tion is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive- aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Bayesian predictive adaptation yields the best performance.This paper is based upon work supported by the EC (FP7) under CasMaCat (287576) project and the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV "Consolider Ingenio 2010" (CSD2007-00018) and iTrans2 (TIN2009-14511). This work is also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under Grant Prometeo/2009/014, and by the UPV under Grant 20091027. The authors would like to thank the anonymous reviewers for their useful and constructive comments.Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2012). Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recognition. 45(9):3193-3203. https://doi.org/10.1016/j.patcog.2012.01.011S3193320345

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Modeling Target-Side Inflection in Neural Machine Translation

NMT systems have problems with large vocabulary sizes. Byte-pair encoding (BPE) is a popular approach to solving this problem, but while BPE allows the system to generate any target-side word, it does not enable effective generalization over the rich vocabulary in morphologically rich languages with strong inflectional phenomena. We introduce a simple approach to overcome this problem by training a system to produce the lemma of a word and its morphologically rich POS tag, which is then followed by a deterministic generation step. We apply this strategy for English-Czech and English-German translation scenarios, obtaining improvements in both settings. We furthermore show that the improvement is not due to only adding explicit morphological information.Comment: Accepted as a research paper at WMT17. (Updated version with corrected references.

Comparison between statistical and neuronal models for machine translation

[EN] Machine translation is a thriving field that deals with multiple of the challenges that the modern world face. From accessing to knowledge in a foreign language, to being able to communicate with people that does not speakthelanguage, we can take great benefit from automatic translation made by software. The state-of-the-art models of machine translation during the last decades, based of inferred statistical knowledge over a set of parallel data, had been recently challenged by neural models based on large artificial neural networks. This study aims to compare both methods of machine translation, the one based on statistical inference (SMT) and the one based on neural networks (NMT). The objective of the project is to compare the performance and the computational needs of both models depending on different factors like the size of the training data or the likeliness of language pair. To make this comparison I have used publicly available parallel data and frameworks in order to implement the models. The evaluation of said models are done under the BLEU score, which computes the correspondence of the translation with the translation made by a human operation. The results indicate that the SMT model outperform the NMT model given relatively small amount of data and a basic set of techniques. The results also shown that NMT have a substantially higher need of processing power, given that the training of large ANN is more demanding than the statistical inference[ES] La traducción automática (TA) es el uso de software para traducir desde un idioma a otro. El objetivo de realizar traducciones automaticas entre idiomas se remonta a los inicios de los computadores electrónicos. La TA ha evolucionado desde sus inicios en los años 50 reflejando los avances en el campo de la computación. En los años 80 un equipo dirigido por Makoro Nagao desarrolló el primer sistema que basaba la traducción en la analogía entre textos traducidos. Este fue el primer sistema de traducción automática estadística (TAE). La idea básica detrás de la TAE es usar las distribuciones de probabilidades extraídas de los textos traducidos para crear un modelo de traducción. Los sistemas de TAE han sido los sistemas de TA más estudiados y el estándard de estas ultimas décadas. No obstante, con la rápida expansión de los sistemas neuronales en la computación, hemos visto un rápido incremento de la traducción automática neuronal (TAN) con grandes empresas como Google cambiando sus sistemas de traducción de los previos modelos estadísticos a modelos neuronales. El objetivo de este proyecto es comparar la TAE y la TAN. La TAE usa la probabilidad como base de su traducción mientras que la TAN usa grandes redes neuronales. Con esta comparación espero ganar un profundo conocimiento sobre como los diferentes algoritmos y parámetros de los dos métodos afectan a sus traducciones. Con los nuevos avances computacionales y en un mundo más global que nunca, la TA es un campo prospero. Comparar estos dos métodos nos puede dar la información necesaria para decidir cual usar dependiendo de nuestra situación y limitaciones. Además, entender la evolución de un campo como el de la TA nos puede ayudar a visualizar futuros cambios e identificar áreas de investigación interesantes. En este proyecto compararé la TAE con la TAN. El alcance de esta comparación incluye (pero no está limitado a) los fundamentos de los modelos, su efectividad, la cantidad de recursos computacionales que necesitan y la cantidad de datos de entrenamiento que necesitan. Consecuentemente, el problema puede ser definido como: Cuales son las principales diferencias entre la TAE y la TAN y como se desempeñan estos métdos con diferentes idiomas y diferentes cantidades de recursos como el tamaño de los datos de entrenamiento . Para la comparación usare distintos marcos de trabajo como MOSES para estudiar las traducciones de métodos de TAE o OpenNMT para la TAN. Respecto a los datos de entrenamiento, me centraré en los conjuntos de datos proporcionados para el workshop en TAE (WMT), concretamente aquellos textos con traducciones de noticias. Una de las principales comparaciones será ir incrementando el tamaño de los datos de entrenamiento para ver como influye en la calidad de la traducción y en la necesidad de recursos computacionales. La evaluación de la traducción es una tarea compleja y un campo de investigación por si mismo dentro de la TM. Para este proyecto usare el método BLEU. Otra comparación importante es comparar como los modelos se desempeñan con pares de idiomas más sencillos como Inglés y Alemán en comparación a como lo hacen con pares más complejos como Chino y Inglés.Llorens Ripollés, JM. (2018). Comparison between statistical and neuronal models for machine translation. http://hdl.handle.net/10251/107663TFG