Salma Jamoussi


2015

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Machine Translation Experiments on PADIC: A Parallel Arabic DIalect Corpus
Karima Meftouh | Salima Harrat | Salma Jamoussi | Mourad Abbas | Kamel Smaili
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Arabic-English Semantic Word Class Alignment to Improve Statistical Machine Translation
Ines Turki Khemakhem | Salma Jamoussi | Abdelmajid Ben Hamadou
Proceedings of the International Conference Recent Advances in Natural Language Processing

2014

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Phrase-based language modelling for statistical machine translation
Achraf Ben Romdhane | Salma Jamoussi | Abdelmajid Ben Hamadou | Kamel Smaïli
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present our submitted MT system for the IWSLT2014 Evaluation Campaign. We participated in the English-French translation task. In this article we focus on one of the most important component of SMT: the language model. The idea is to use a phrase-based language model. For that, sequences from the source and the target language models are retrieved and used to calculate a phrase n-gram language model. These phrases are used to rewrite the parallel corpus which is then used to calculate a new translation model.

2013

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Integrating morpho-syntactic features in English-Arabic statistical machine translation
Ines Turki Khemakhem | Salma Jamoussi | Abdelmajid Ben Hamadou
Proceedings of the Second Workshop on Hybrid Approaches to Translation

2010

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Arabic morpho-syntactic feature disambiguation in a translation context
Ines Turki Khemakhem | Salma Jamoussi | Abdelmajid Ben Hamadou
Proceedings of the 4th Workshop on Syntax and Structure in Statistical Translation

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The MIRACL Arabic-English statistical machine translation system for IWSLT 2010
Ines Turki Khemakhem | Salma Jamoussi | Abdelmajid Ben Hamadou
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the MIRACL statistical Machine Translation system and the improvements that were developed during the IWSLT 2010 evaluation campaign. We participated to the Arabic to English BTEC tasks using a phrase-based statistical machine translation approach. In this paper, we first discuss some challenges in translating from Arabic to English and we explore various techniques to improve performances on a such task. Next, we present our solution for disambiguating the output of an Arabic morphological analyzer. In fact, The Arabic morphological analyzer used produces all possible morphological structures for each word, with an unique correct proposition. In this work we exploit the Arabic-English alignment to choose the correct segmented form and the correct morpho-syntactic features produced by our morphological analyzer.

2004

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A Complete Understanding Speech System Based on Semantic Concepts
Salma Jamoussi | Kamel Smaïli | Dominique Fohr | Jean-Paul Haton
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2003

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Vers la compréhension automatique de la parole : extraction de concepts par réseaux bayésiens
Salma Jamoussi | Kamel Smaïli | Jean-Paul Haton
Actes de la 10ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

La compréhension automatique de la parole peut être considérée comme un problème d’association entre deux langages différents. En entrée, la requête exprimée en langage naturel et en sortie, juste avant l’étape d’interprétation, la même requête exprimée en terme de concepts. Un concept représente un sens bien déterminé. Il est défini par un ensemble de mots partageant les mêmes propriétés sémantiques. Dans cet article, nous proposons une méthode à base de réseau bayésien pour l’extraction automatique des concepts ainsi que trois approches différentes pour la représentation vectorielle des mots. Ces représentations aident un réseau bayésien à regrouper les mots, construisant ainsi la liste adéquate des concepts à partir d’un corpus d’apprentissage. Nous conclurons cet article par la description d’une étape de post-traitement au cours de laquelle, nous étiquetons nos requêtes et nous générons les commandes SQL appropriées validant ainsi, notre approche de compréhension.