Jean Senellart


2020

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Integrating Domain Terminology into Neural Machine Translation
Elise Michon | Josep Crego | Jean Senellart
Proceedings of the 28th International Conference on Computational Linguistics

This paper extends existing work on terminology integration into Neural Machine Translation, a common industrial practice to dynamically adapt translation to a specific domain. Our method, based on the use of placeholders complemented with morphosyntactic annotation, efficiently taps into the ability of the neural network to deal with symbolic knowledge to surpass the surface generalization shown by alternative techniques. We compare our approach to state-of-the-art systems and benchmark them through a well-defined evaluation framework, focusing on actual application of terminology and not just on the overall performance. Results indicate the suitability of our method in the use-case where terminology is used in a system trained on generic data only.

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Boosting Neural Machine Translation with Similar Translations
Jitao Xu | Josep Crego | Jean Senellart
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper explores data augmentation methods for training Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. In particular, we show how we can simply present the neural model with information of both source and target sides of the fuzzy matches, we also extend the similarity to include semantically related translations retrieved using sentence distributed representations. We show that translations based on fuzzy matching provide the model with “copy” information while translations based on embedding similarities tend to extend the translation “context”. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, combine naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements. To foster research around these techniques, we also release an Open-Source toolkit with efficient and flexible fuzzy-match implementation.

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Priming Neural Machine Translation
Minh Quang Pham | Jitao Xu | Josep Crego | François Yvon | Jean Senellart
Proceedings of the Fifth Conference on Machine Translation

Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT). We evaluate the effect of using similar translations as priming cues on the NMT network. We propose a method to inject priming cues into the NMT network and compare our framework to other mechanisms that perform micro-adaptation during inference. Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy. Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.

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A Study of Residual Adapters for Multi-Domain Neural Machine Translation
Minh Quang Pham | Josep Maria Crego | François Yvon | Jean Senellart
Proceedings of the Fifth Conference on Machine Translation

Domain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the mode unchanged. This has the additional merit to leave the baseline model intact, and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea on two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model, and open perspective to also make adapted models more robust to label domain errors.

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The OpenNMT Neural Machine Translation Toolkit: 2020 Edition
Guillaume Klein | François Hernandez | Vincent Nguyen | Jean Senellart
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Efficient and High-Quality Neural Machine Translation with OpenNMT
Guillaume Klein | Dakun Zhang | Clément Chouteau | Josep Crego | Jean Senellart
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task. We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup. We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies. By combining additional optimizations and parallelization techniques, we create small, efficient, and high-quality neural machine translation models.

2019

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SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task
Jitao Xu | TuAnh Nguyen | MinhQuang Pham | Josep Crego | Jean Senellart
Proceedings of the 6th Workshop on Asian Translation

This paper describes Systran’s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.

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Enhanced Transformer Model for Data-to-Text Generation
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records. In this work, we present a new Transformer-based data-to-text generation model which learns content selection and summary generation in an end-to-end fashion. We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling. In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models. Evaluation experiments show that our final model outperforms current state-of-the-art systems as measured by different metrics: BLEU, content selection precision and content ordering. We made publicly available the transformer extension presented in this paper.

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SYSTRAN @ WNGT 2019: DGT Task
Li Gong | Josep Crego | Jean Senellart
Proceedings of the 3rd Workshop on Neural Generation and Translation

This paper describes SYSTRAN participation to the Document-level Generation and Trans- lation (DGT) Shared Task of the 3rd Workshop on Neural Generation and Translation (WNGT 2019). We participate for the first time using a Transformer network enhanced with modified input embeddings and optimising an additional objective function that considers content selection. The network takes in structured data of basketball games and outputs a summary of the game in natural language.

2018

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OpenNMT: Neural Machine Translation Toolkit
Guillaume Klein | Yoon Kim | Yuntian Deng | Vincent Nguyen | Jean Senellart | Alexander Rush
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Jean Senellart | Dakun Zhang | Bo Wang | Guillaume Klein | Jean-Pierre Ramatchandirin | Josep Crego | Alexander Rush
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.

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Neural Network Architectures for Arabic Dialect Identification
Elise Michon | Minh Quang Pham | Josep Crego | Jean Senellart
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

SYSTRAN competes this year for the first time to the DSL shared task, in the Arabic Dialect Identification subtask. We participate by training several Neural Network models showing that we can obtain competitive results despite the limited amount of training data available for learning. We report our experiments and detail the network architecture and parameters of our 3 runs: our best performing system consists in a Multi-Input CNN that learns separate embeddings for lexical, phonetic and acoustic input features (F1: 0.5289); we also built a CNN-biLSTM network aimed at capturing both spatial and sequential features directly from speech spectrograms (F1: 0.3894 at submission time, F1: 0.4235 with later found parameters); and finally a system relying on binary CNN-biLSTMs (F1: 0.4339).

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SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering
MinhQuang Pham | Josep Crego | Jean Senellart
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the participation of SYSTRAN to the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018). We participate for the first time using a neural sentence similarity classifier which aims at predicting the relatedness of sentence pairs in a multilingual context. The paper describes the main characteristics of our approach and discusses the results obtained on the data sets published for the shared task.

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Fixing Translation Divergences in Parallel Corpora for Neural MT
MinhQuang Pham | Josep Crego | Jean Senellart | François Yvon
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Corpus-based approaches to machine translation rely on the availability of clean parallel corpora. Such resources are scarce, and because of the automatic processes involved in their preparation, they are often noisy. This paper describes an unsupervised method for detecting translation divergences in parallel sentences. We rely on a neural network that computes cross-lingual sentence similarity scores, which are then used to effectively filter out divergent translations. Furthermore, similarity scores predicted by the network are used to identify and fix some partial divergences, yielding additional parallel segments. We evaluate these methods for English-French and English-German machine translation tasks, and show that using filtered/corrected corpora actually improves MT performance.

2017

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SYSTRAN Purely Neural MT Engines for WMT2017
Yongchao Deng | Jungi Kim | Guillaume Klein | Catherine Kobus | Natalia Segal | Christophe Servan | Bo Wang | Dakun Zhang | Josep Crego | Jean Senellart
Proceedings of the Second Conference on Machine Translation

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OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein | Yoon Kim | Yuntian Deng | Jean Senellart | Alexander Rush
Proceedings of ACL 2017, System Demonstrations

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Domain Control for Neural Machine Translation
Catherine Kobus | Josep Crego | Jean Senellart
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have already been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.

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Boosting Neural Machine Translation
Dakun Zhang | Jungi Kim | Josep Crego | Jean Senellart
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

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Adaptation incrémentale de modèles de traduction neuronaux (Incremental adaptation of neural machine translation models)
Christophe Servan | Josep Crego | Jean Senellart
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

L’adaptation au domaine est un verrou scientifique en traduction automatique. Il englobe généralement l’adaptation de la terminologie et du style, en particulier pour la post-édition humaine dans le cadre d’une traduction assistée par ordinateur. Avec la traduction automatique neuronale, nous étudions une nouvelle approche d’adaptation au domaine que nous appelons “spécialisation” et qui présente des résultats prometteurs tant dans la vitesse d’apprentissage que dans les scores de traduction. Dans cet article, nous proposons d’explorer cette approche.

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Conception d’une solution de détection d’événements basée sur Twitter (Design of a solution for event detection from Tweeter)
Christophe Servan | Catherine Kobus | Yongchao Deng | Cyril Touffet | Jungi Kim | Inès Kapp | Djamel Mostefa | Josep Crego | Aurélien Coquard | Jean Senellart
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations

Cet article présente un système d’alertes fondé sur la masse de données issues de Tweeter. L’objectif de l’outil est de surveiller l’actualité, autour de différents domaines témoin incluant les événements sportifs ou les catastrophes naturelles. Cette surveillance est transmise à l’utilisateur sous forme d’une interface web contenant la liste d’événements localisés sur une carte.

2012

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Incremental adaptation using translation information and post-editing analysis
Frédéric Blain | Holger Schwenk | Jean Senellart
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

It is well known that statistical machine translation systems perform best when they are adapted to the task. In this paper we propose new methods to quickly perform incremental adaptation without the need to obtain word-by-word alignments from GIZA or similar tools. The main idea is to use an automatic translation as pivot to infer alignments between the source sentence and the reference translation, or user correction. We compared our approach to the standard method to perform incremental re-training. We achieve similar results in the BLEU score using less computational resources. Fast retraining is particularly interesting when we want to almost instantly integrate user feed-back, for instance in a post-editing context or machine translation assisted CAT tool. We also explore several methods to combine the translation models.

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Joint WMT 2012 Submission of the QUAERO Project
Markus Freitag | Stephan Peitz | Matthias Huck | Hermann Ney | Jan Niehues | Teresa Herrmann | Alex Waibel | Hai-son Le | Thomas Lavergne | Alexandre Allauzen | Bianka Buschbeck | Josep Maria Crego | Jean Senellart
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Collaborative Machine Translation Service for Scientific texts
Patrik Lambert | Jean Senellart | Laurent Romary | Holger Schwenk | Florian Zipser | Patrice Lopez | Frédéric Blain
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Joint WMT Submission of the QUAERO Project
Markus Freitag | Gregor Leusch | Joern Wuebker | Stephan Peitz | Hermann Ney | Teresa Herrmann | Jan Niehues | Alex Waibel | Alexandre Allauzen | Gilles Adda | Josep Maria Crego | Bianka Buschbeck | Tonio Wandmacher | Jean Senellart
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Qualitative Analysis of Post-Editing for High Quality Machine Translation
Frédéric Blain | Jean Senellart | Holger Schwenk | Mirko Plitt | Johann Roturier
Proceedings of Machine Translation Summit XIII: Papers

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Advances on spoken language translation in the Quaero program
Karim Boudahmane | Bianka Buschbeck | Eunah Cho | Josep Maria Crego | Markus Freitag | Thomas Lavergne | Hermann Ney | Jan Niehues | Stephan Peitz | Jean Senellart | Artem Sokolov | Alex Waibel | Tonio Wandmacher | Joern Wuebker | François Yvon
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

The Quaero program is an international project promoting research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within the program framework, research organizations and industrial partners collaborate to develop prototypes of innovating applications and services for access and usage of multimedia data. One of the topics addressed is the translation of spoken language. Each year, a project-internal evaluation is conducted by DGA to monitor the technological advances. This work describes the design and results of the 2011 evaluation campaign. The participating partners were RWTH, KIT, LIMSI and SYSTRAN. Their approaches are compared on both ASR output and reference transcripts of speech data for the translation between French and German. The results show that the developed techniques further the state of the art and improve translation quality.

2010

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Convergence of Translation Memory and Statistical Machine Translation
Philipp Koehn | Jean Senellart
Proceedings of the Second Joint EM+/CNGL Workshop: Bringing MT to the User: Research on Integrating MT in the Translation Industry

We present two methods that merge ideas from statistical machine translation (SMT) and translation memories (TM). We use a TM to retrieve matches for source segments, and replace the mismatched parts with instructions to an SMT system to fill in the gap. We show that for fuzzy matches of over 70%, one method outperforms both SMT and TM baselines.

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Fast Approximate String Matching with Suffix Arrays and A* Parsing
Philipp Koehn | Jean Senellart
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

We present a novel exact solution to the approximate string matching problem in the context of translation memories, where a text segment has to be matched against a large corpus, while allowing for errors. We use suffix arrays to detect exact n-gram matches, A* search heuristics to discard matches and A* parsing to validate candidate segments. The method outperforms the canonical baseline by a factor of 100, with average lookup times of 4.3–247ms for a segment in a realistic scenario.

2009

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Selective addition of corpus-extracted phrasal lexical rules to a rule-based machine translation system
Loic Dugast | Jean Senellart | Philipp Koehn
Proceedings of Machine Translation Summit XII: Posters

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Translation Model Adaptation for an Arabic/French News Translation System by Lightly- Supervised Training
Holger Schwenk | Jean Senellart
Proceedings of Machine Translation Summit XII: Posters

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Statistical Post Editing and Dictionary Extraction: Systran/Edinburgh Submissions for ACL-WMT2009
Loic Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Fourth Workshop on Statistical Machine Translation

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SMT and SPE Machine Translation Systems for WMT‘09
Holger Schwenk | Sadaf Abdul-Rauf | Loïc Barrault | Jean Senellart
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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First Steps towards a General Purpose French/English Statistical Machine Translation System
Holger Schwenk | Jean-Baptiste Fouet | Jean Senellart
Proceedings of the Third Workshop on Statistical Machine Translation

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Can we Relearn an RBMT System?
Loïc Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Third Workshop on Statistical Machine Translation

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Tighter Integration of Rule-Based and Statistical MT in Serial System Combination
Nicola Ueffing | Jens Stephan | Evgeny Matusov | Loïc Dugast | George Foster | Roland Kuhn | Jean Senellart | Jin Yang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Statistical Post-Editing on SYSTRAN‘s Rule-Based Translation System
Loïc Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Second Workshop on Statistical Machine Translation

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Rapid development of new language pairs at SYSTRAN
Sylvain Surcin | Elke Lange | Jean Senellart
Proceedings of Machine Translation Summit XI: Papers

2006

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First strategies for integrating hybrid approaches into established systems
Jean Senellart | John S. White
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Panel on hybrid machine translation: why and how?

2005

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Integration of SYSTRAN MT Systems in an Open Workflow
Mats Attnäs | Pierre Senellart | Jean Senellart
Proceedings of Machine Translation Summit X: Papers

2003

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SYSTRAN new generation: the XML translation workflow
Jean Senellart | Christian Boitet | Laurent Romary
Proceedings of Machine Translation Summit IX: Papers

Customization of Machine Translation (MT) is a prerequisite for corporations to adopt the technology. It is therefore important but nonetheless challenging. Ongoing implementation proves that XML is an excellent exchange device between MT modules that efficiently enables interaction between the user and the processes to reach highly granulated structure-based customization. Accomplished through an innovative approach called the SYSTRAN Translation Stylesheet, this method is coherent with the current evolution of the “authoring process”. As a natural progression, the next stage in the customization process is the integration of MT in a multilingual tool kit designed for the “authoring process”.

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SYSTRAN intuitive coding technology
Jean Senellart | Jin Yang | Anabel Rebollo
Proceedings of Machine Translation Summit IX: Papers

Customizing a general-purpose MT system is an effective way to improve machine translation quality for specific usages. Building a user-specific dictionary is the first and most important step in the customization process. An intuitive dictionary-coding tool was developed and is now utilized to allow the user to build user dictionaries easily and intelligently. SYSTRAN’s innovative and proprietary IntuitiveCoding® technology is the engine powering this tool. It is comprised of various components: massive linguistic resources, a morphological analyzer, a statistical guesser, finite-state automaton, and a context-free grammar. Methodologically, IntuitiveCoding® is also a cross-application approach for high quality dictionary building in terminology import and exchange. This paper describes the various components and the issues involved in its implementation. An evaluation frame and utilization of the technology are also presented.

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Inductive coding of the Arabic lexicon
Ali Farghaly | Jean Senellart
Workshop on Machine Translation for Semitic languages: issues and approaches

SYSTRAN started the design and the development of Arabic, Farsi and Urdu to English machine translation systems in July 2002. This paper describes the methodology and implementation adopted for dictionary building and morphological analysis. SYSTRAN’s IntuitiveCoding® technology (ICT) for facilitates the creation, update, and maintenance of Arabic, Farsi and Urdu lexical entries, is more modular and less costly. ICT for Arabic, Farsi, and Urdu requires the implementation of stem-based lexical entries, the authentic scripts for each language, a statistical Arabic stem-guesser, and separate declarative modules for internal and external morphology.

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SYSTRAN’s Chinese Word Segmentation
Jin Yang | Jean Senellart | Remi Zajac
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2001

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New generation Systran translation system
Jean Senellart | Péter Dienes | Tamás Váradi
Proceedings of Machine Translation Summit VIII

In this paper, we present the design of the new generation Systran translation systems, currently utilized in the development of English-Hungarian, English-Polish, English-Arabic, French-Arabic, Hungarian-French and Polish-French language pairs. The new design, based on the traditional Systran machine translation expertise and the existing linguistic resources, addresses the following aspects: efficiency, modularity, declarativity, reusability, and maintainability. Technically, the new systems rely on intensive use of state-of-the-art finite automaton and formal grammar implementation. The finite automata provide the essential lookup facilities and the natural capacity of factorizing intuitive linguistic sets. Linguistically, we have introduced a full monolingual description of linguistic information and the concept of implicit transfer. Finally, we present some by-products that are directly derived from the new architecture: intuitive coding tools, spell checker and syntactic tagger.

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Resource alignment for machine translation or implicit transfer
Jean Senellart | Mirko Plitt | Christophe Bailly | Françoise Cardoso
Proceedings of Machine Translation Summit VIII

In this article we present the concept of “implicit transfer” rules. We will show that they represent a valid compromise between huge direct transfer terminology lists and large sets of transfer rules, which are very complex to maintain. We present a concrete, real-life application of this concept in a customization project (TOLEDO project) concerning the automatic translation of Autodesk (ADSK) support pages. In this application, the alignment is moreover combined with a graph representation substituting linear dictionaries. We show how the concept could be extended to increase coverage of traditional translation dictionaries as well as to extract terminology from large existing multilingual corpora. We also introduce the concept of "alignment dictionary" which seems promising in its ability to extend the pragmatic limits of multilingual dictionary management.

1999

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Semi-automatic acquisition of lexical resources for new languages or new domains
Jean Senellart
EAMT Workshop: EU and the new languages

1998

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Locating noun phrases with finite state transducers.
Jean Senellart
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Locating Noun Phrases with Finite State Transducers
Jean Senellart
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Tools for locating noun phrases with finite state transducers
Jean Senellart
The Computational Treatment of Nominals