Études & Publications

À propos de SYSTRAN

Depuis sa création en 1968, SYSTRAN est pionnier dans le domaine des technologies de traduction automatique. Les innovations développées par SYSTRAN sont désormais couramment utilisées par les entreprises et utilisateurs professionnels. Avec une forte orientation vers la recherche et le développement, SYSTRAN, 50 ans plus tard, est plus innovante que jamais.

SYSTRAN propose aux utilisateurs professionnels des solutions de traduction automatisée avancées et sécurisées dans divers domaines tels que la collaboration à l'international, la production de contenu multilingue, le support client, l'eDiscovery, l'analyse de Big Data, le e-commerce, etc. SYSTRAN propose une solution sur-mesure avec une architecture ouverte et évolutive qui permet une intégration transparente dans les applications tierces existantes et les infrastructures informatiques.

Enhanced Transformer Model for Data-to-Text Generation

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 … Suite

Li Gong, Josep Crego, Jean Senellart

Book: Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 148--156, Association for Computational Linguistics, November 2019, Hong-Kong, China

SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task

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 … Suite

Jitao Xu, TuAnh Nguyen, MinhQuang Pham, Josep Crego, Jean Senellart

Proceedings of the 6th Workshop on Asian Translation, pages 189--194, Association for Computational Linguistics, November 2019, Hong-Kong, China

SYSTRAN @ WNGT 2019: DGT Task

This paper describes SYSTRAN participation to the Document-level Generation and Translation (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 … Suite

Li Gong, Josep Crego, Jean Senellart

Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 262--267, Association for Computational Linguistics, November 2019, Hong-Kong, China

SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering

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 … Suite

Minh Quang Pham, Josep Crego, Jean Senellart

Third Conference on Machine Translation (WMT18), October 31 - November 1 2018, Brussels, Belgium

Fixing Translation Divergences in Parallel Corpora for Neural MT

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. % may contain sentence pairs that are not as parallel as one would expect. This paper describes an unsupervised method for detecting translation divergences … Suite

Minh Quang Pham, Josep Crego, Jean Senellart, François Yvon

2018 Conference on Empirical Methods in Natural Language Processing, October 31 – November 4 2018, Brussels, Belgium

Analyzing Knowledge Distillation in Neural Machine Translation

Knowledge distillation has recently been successfully applied to neural machine translation. It basically allows for building shrunk networks while the resulting systems retain most of the quality of the original model. Despite that many authors report on the benefits of knowledge distillation, few works discuss the actual reasons why it works, especially in the context … Suite

Dakun Zhang, Josep Crego and Jean Senellart

15th International Workshop on Spoken Language Translation, October 29-30 2018, Bruges, Belgium

OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU

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 leading to significant speed-ups in combination: (a) sequence distillation, (b) … Suite

Jean Senellart, Dakun Zhang, Bo Wang, Guillaume Klein, J.P. Ramatchandirin, Josep Crego, Alexander M. Rush

Published in "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation", pages 122-–128, Association for Computational Linguistics, July 20 2018, Melbourne, Australia

Neural Network Architectures for Arabic Dialect Identification

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 … Suite

Elise Michon, Minh Quang Pham, Josep Crego, Jean Senellart

Published in "Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects", Association for Computational Linguistics, pages 128-–136, August 20 2018, New Mexico, USA

Boosting Neural Machine Translation [PDF]

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 … Suite

Dakun Zhang, Jungi Kim, Josep Crego, Jean Senellart

Published in "Proceedings of the Eighth International Joint Conference on Natural Language Processing" (Volume 2: Short Papers), Asian Federation of Natural Language Processing, 2017, Taipei, Taiwan

OpenNMT: Open-Source Toolkit for Neural Machine Translation [PDF]

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about … Suite

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander Rush

Published in "Proceedings of ACL 2017, System Demonstrations", pages 67--72, Association for Computational Linguistics, 2017, Vancouver, Canada