UPM system for the translation task

This paper describes the UPM system for translation task at the EMNLP 2011 workshop on statistical machine translation (http://www.statmt.org/wmt11/), and it has been used for both directions: Spanish-English and English-Spanish. This system is based on Moses with two new modules for pre and post processing the sentences. The main contribution is the method proposed (based on the similarity with the source language test set) for selecting the sentences for training the models and adjusting the weights. With system, we have obtained a 23.2 BLEU for Spanish-English and 21.7 BLEU for EnglishSpanis

UPM system for WMT 2012

This paper describes the UPM system for the Spanish-English translation task at the NAACL 2012 workshop on statistical machine translation. This system is based on Moses. We have used all available free corpora, cleaning and deleting some repetitions. In this paper, we also propose a technique for selecting the sentences for tuning the system. This technique is based on the similarity with the sentences to translate. With our approach, we improve the BLEU score from 28.37% to 28.57%. And as a result of the WMT12 challenge we have obtained a 31.80% BLEU with the 2012 test set. Finally, we explain different experiments that we have carried out after the competition

Clustering of syntactic and discursive information for the dynamic adaptation of Language Models

Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase

Spanish generation from Spanish Sign Language using a phrase-based translation system

This paper describes the development of a Spoken Spanish generator from Spanish Sign Language (LSE – Lengua de Signos Española) in a specific domain: the renewal of Identity Document and Driver’s license. The system is composed of three modules. The first one is an interface where a deaf person can specify a sign sequence in sign-writing. The second one is a language translator for converting the sign sequence into a word sequence. Finally, the last module is a text to speech converter. Also, the paper describes the generation of a parallel corpus for the system development composed of more than 4,000 Spanish sentences and their LSE translations in the application domain. The paper is focused on the translation module that uses a statistical strategy with a phrase-based translation model, and this paper analyses the effect of the alignment configuration used during the process of word based translation model generation. Finally, the best configuration gives a 3.90% mWER and a 0.9645 BLEU

Detecting acronyms from capital letter sequences in Spanish

This paper presents an automatic strategy to decide how to pronounce a Capital Letter Sequence (CLS) in a Text to Speech system (TTS). If CLS is well known by the TTS, it can be expanded in several words. But when the CLS is unknown, the system has two alternatives: spelling it (abbreviation) or pronouncing it as a new word (acronym). In Spanish, there is a high relationship between letters and phonemes. Because of this, when a CLS is similar to other words in Spanish, there is a high tendency to pronounce it as a standard word. This paper proposes an automatic method for detecting acronyms. Additionaly, this paper analyses the discrimination capability of some features, and several strategies for combining them in order to obtain the best classifier. For the best classifier, the classification error is 8.45%. About the feature analysis, the best features have been the Letter Sequence Perplexity and the Average N-gram order

Evaluating a Speech Communication System for deaf people

This paper describes the development of an Advanced Speech Communication System for Deaf People and its field evaluation in a real application domain: the renewal of Driver’s License. The system is composed of two modules. The first one is a Spanish into Spanish Sign Language (LSE: Lengua de Signos Española) translation module made up of a speech recognizer, a natural language translator (for converting a word sequence into a sequence of signs), and a 3D avatar animation module (for playing back the signs). The second module is a Spoken Spanish generator from sign-writing composed of a visual interface (for specifying a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, a text to speech converter. For language translation, the system integrates three technologies: an example-based strategy, a rule-based translation method and a statistical translator. This paper also includes a detailed description of the evaluation carried out in the Local Traffic Office in the city of Toledo (Spain) involving real government employees and deaf people. This evaluation includes objective measurements from the system and subjective information from questionnaires. Finally, the paper reports an analysis of the main problems and a discussion about possible solutions

Sistema de Comunicación Oral para Personas Sordas

Este artículo describe el desarrollo y la evaluación de un sistema de comunicación para personas sordas en un ámbito de aplicación específico: la renovación del permiso de conducir. El sistema de comunicación desarrollado está compuesto por dos módulos que permiten la comunicación en los dos sentidos. El primer módulo es un traductor de voz en castellano a Lengua de Signos Española (LSE) y está formado por un reconocedor de voz, un traductor de palabras en castellano a una secuencia de signos y un tercer módulo de representación de los signos mediante un agente animado. El segundo módulo es un generador de voz en castellano a partir de una secuencia de signos, y está formado por una interfaz donde se especifican los signos, un traductor (para convertir la secuencia de signos en una secuencia de palabras) y un conversor de texto a voz. En los dos módulos de traducción entre lenguas, se integran tres tecnologías: una basada en ejemplos, una basada en reglas y un traductor estadístico. Este artículo describe la evaluación del sistema llevada a cabo en la Jefatura Provincial de Tráfico de Toledo implicando a funcionarios de dicha jefatura y personas sordas

Sentence selection for improving the tuning process of a statistical machine translation system

Este artículo describe una estrategia de selección de frases para hacer el ajuste de un sistema de traducción estadístico basado en el decodificador Moses que traduce del español al inglés. En este trabajo proponemos dos posibilidades para realizar esta selección de las frases del corpus de validación que más se parecen a las frases que queremos traducir (frases de test en lengua origen). Con esta selección podemos obtener unos mejores pesos de los modelos para emplearlos después en el proceso de traducción y, por tanto, mejorar los resultados. Concretamente, con el método de selección basado en la medida de similitud propuesta en este artículo, mejoramos la medida BLEU del 27,17% con el corpus de validación completo al 27,27% seleccionando las frases para el ajuste. Estos resultados se acercan a los del experimento ORACLE: se utilizan las mismas frases de test para hacer el ajuste de los pesos. En este caso, el BLEU obtenido es de 27,51%

Language Resources for Spanish - Spanish Sign Language (LSE) translation

This paper describes the development of a Spanish Spanish Sign Language (LSE) translation system. Firstly, it describes the first Spanish Spanish Sign Language (LSE) parallel corpus focused on two specific domains: the renewal of the Identity Document and Driver’s License. This corpus includes more than 4,000 Spanish sentences (in these domains), their LSE translation and a video for each LSE sentence with the sign language representation. This corpus also contains more than 700 sign descriptions in several sign writing specifications. The translation system developed with this corpus consists of two modules: a Spanish into LSE translation module that is composed of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs) and a 3D avatar animation module (for playing back the signs). The second module is a Spanish generator from LSE made up of a visual interface (for specifying a sequence of signs in sign writing), a language translator (for generating the sequence of words in Spanish) and a text to speech converter. For each language translation, the system uses three technologies: an example based strategy, a rule based translation method and a statistical translator

Advanced Speech Communication System for Deaf People

This paper describes the development of an Advanced Speech Communication System for Deaf People and its field evaluation in a real application domain: the renewal of Driver’s License. The system is composed of two modules. The first one is a Spanish into Spanish Sign Language (LSE: Lengua de Signos Española) translation module made up of a speech recognizer, a natural language translator (for converting a word sequence into a sequence of signs), and a 3D avatar animation module (for playing back the signs). The second module is a Spoken Spanish generator from sign writing composed of a visual interface (for specifying a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, a text to speech converter. For language translation, the system integrates three technologies: an example based strategy, a rule based translation method and a statistical translator. This paper also includes a detailed description of the evaluation carried out in the Local Traffic Office in the city of Toledo (Spain) involving real government employees and deaf people. This evaluation includes objective measurements from the system and subjective information from questionnaire