Optimal bilingual data for French-English PB-SMT

We investigate the impact of the original source language (SL) on French–English PB-SMT. We train four configurations of a state-of-the-art PB-SMT system based on French–English parallel corpora which differ in terms of the original SL, and conduct experiments in both translation directions. We see that data containing original French and English translated from French is optimal when building a system translating from French into English. Conversely, using data comprising exclusively French and English translated from several other languages is suboptimal regardless of the translation direction. Accordingly, the clamour for more data needs to be tempered somewhat; unless the quality of such data is controlled, more training data can cause translation performance to decrease drastically, by up to 38% relative BLEU in our experiments

Données bilingues pour la TAS français-anglais : impact de la langue source et direction de traduction originales sur la qualité de la traduction

In this paper, we argue about the quality of bilingual data for statistical machine translation in terms of the original source language and translation direction in the context of a French to English translation task. We show that data containing original French and English translated from French improves translation quality. Conversely, using data comprising exclusively French and English translated from several other languages results in a clear-cut decrease in translation quality

Comparing constituency and dependency representations for SMT phrase-extraction

We consider the value of replacing and/or combining string-based methods with syntax-based methods for phrase-based statistical machine translation (PBSMT), and we also consider the relative merits of using constituency-annotated vs. dependency-annotated training data. We automatically derive two subtree-aligned treebanks, dependency-based and constituency-based, from a parallel English–French corpus and extract syntactically motivated word- and phrase-pairs. We automatically measure PB-SMT quality. The results show that combining string-based and syntax-based word- and phrase-pairs can improve translation quality irrespective of the type of syntactic annotation. Furthermore, using dependency annotation yields greater translation quality than constituency annotation for PB-SMT

MATREX: the DCU MT System for WMT 2008

In this paper, we give a description of the machine translation system developed at DCU that was used for our participation in the evaluation campaign of the Third Workshop on Statistical Machine Translation at ACL 2008. We describe the modular design of our data driven MT system with particular focus on the components used in this participation. We also describe some of the significant modules which were unused in this task. We participated in the EuroParl task for the following translation directions: Spanish–English and French–English, in which we employed our hybrid EBMT-SMT architecture to translate. We also participated in the Czech–English News and News Commentary tasks which represented a previously untested language pair for our system. We report results on the provided development and test sets

Tracking relevant alignment characteristics for machine translation

In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. In this paper we compare alignments tuned directly according to alignment F-score and BLEU score in order to investigate the alignment characteristics that are helpful in translation. We report results for two different SMT systems (a phrase-based and an n-gram-based system) on Chinese to English IWSLT data, and Spanish to English European Parliament data. We give alignment hints to improve BLEU score, depending on the SMT system used and the type of corpus

Inferring syntactic rules for word alignment through Inductive Logic Programming

International audienceThis paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and is based on a machine-learning technique, namely inductive logic programming (ILP). We show that ILP is particularly well suited for this task in which the data can only be expressed by (translational and syntactic) relations. It allows us to infer easily rules called syntactic alignment rules. These rules make the most of the syntactic information to align words. A simple bootstrapping technique provides the examples needed by ILP, making this machine learning approach entirely automatic. Moreover, through different experiments, we show that this approach requires a very small amount of training data, and its performance rivals some of the best existing alignment systems. Furthermore, cases of syntactic isomorphisms or non-isomorphisms between the source language and the target language are easily identified through the inferred rules

Syntex, analyseur syntaxique de corpus

Cet article est un document de présentation de l'analyseur syntaxique de corpus Syntex, dans lequel nous décrivons les principes à la base du développement de l'analyseur et son architecture informatique. Une bibliographie du projet SYNTEX est donnée à la fin du document

Données bilingues pour la TAS français-anglais : impact de la langue source et direction de traduction originales sur la qualité de la traduction

In this paper, we argue about the quality of bilingual data for statistical machine translation in terms of the original source language and translation direction in the context of a French to English translation task. We show that data containing original French and English translated from French improves translation quality. Conversely, using data comprising exclusively French and English translated from several other languages results in a clear-cut decrease in translation quality