Julien Bourdaillet

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As basic as bilingual concordancers may appear, they are some of the most widely used computer-assisted translation tools among professional translators. Nevertheless, they still do not benefit from recent breakthroughs in machine translation. This paper describes the improvement of the commercial bilingual concordancer TransSearch in order to embed a word(More)
Despite the impressive amount of recent studies devoted to improving the state of the art of Machine Translation (MT), Computer Assisted Translation (CAT) tools remain the preferred solution of human translators when publication quality is of concern. In this paper, we present our perspectives on improving the commercial bilingual concordancer TransSearch,(More)
We describe our system for the translation task of WMT 2010. This system, developed for the English-French and FrenchEnglish directions, is based on Moses and was trained using only the resources supplied for the workshop. We report experiments to enhance it with out-of-domain parallel corpora sub-sampling, N-best list post-processing and a French(More)
The phrase-based translation approach has overcome several drawbacks of the word-based translation methods and proved to significantly improve the quality of translated output. However, they show less improvement on translating between languages with very different syntax and morphology, especially when the translation direction is from a language with(More)
This paper presents a joint work between artificial intelligence and literary studies. As part of the humanities, textual genetic criticism deals with writers’ rewriting processes. By studying drafts and manuscripts issued from these processes, the genesis of the text is discovered. When draft comparison is done manually, it requires a huge amount of work.(More)
Crowdsourcing translation tasks typically face issues due to poor quality and spam translations. We propose a novel method for generating large multilingual text corpora leveraging Tournament Selection and LatticeBased String Alignment without requiring expert involvement or Gold data. We use crowdsourcing for gathering a set of candidate translations of a(More)
Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We(More)