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Über Systran

SYSTRAN verfügt über mehr als 50 Jahre Erfahrung in Übersetzungstechnologien und leistet Pionierarbeit bei den größten Innovationen in diesem Bereich. Dazu gehören die ersten webbasierten Übersetzungsportale und die ersten neuronalen Übersetzungsmodule, die künstliche Intelligenz und neuronale Netzwerke für Unternehmen und öffentliche Organisationen kombinieren.

SYSTRAN stellt Geschäftskunden hoch entwickelte und sichere automatisierte Übersetzungslösungen in verschiedenen Bereichen zur Verfügung, wie z. B. globale Zusammenarbeit, Erstellung mehrsprachiger Inhalte, Kundensupport, elektronische Ermittlungen, Big Data-Analyse, E-Commerce, etc. SYSTRAN bietet eine maßgeschneiderte Lösung mit einer offenen und skalierbaren Architektur, die eine nahtlose Integration in bestehende Anwendungen und IT-Infrastrukturen von Drittanbietern ermöglicht.

Rosetta-LSF: Ein abgestimmtes Corpus aus französischer Gebärdensprache und Französisch für Text-zu-Zeichen-Übersetzung

Rosetta-LSF: Ein abgestimmtes Corpus aus französischer Gebärdensprache und Französisch für Text-zu-Zeichen-Übersetzung

Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, Jérémie Segouat.

13th Conference on Language Resources and Evaluation (LREC 2022), Jun 2022, Marseille, France.

Joint Generation of Captions and Subtitles with Dual Decoding

Joint Generation of Captions and Subtitles with Dual Decoding

As the amount of audio-visual content increases, the need to develop automatic captioning and subtitling solutions to match the expectations of a growing international audience appears as the only viable way to boost throughput and lower the related post-production costs. Automatic captioning and subtitling often need to be tightly intertwined to achieve an appropriate level … Fortsetzung

Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), May 2022, Dublin, Ireland

SYSTRAN @ WMT 2021: Terminology Task

SYSTRAN @ WMT 2021: Terminology Task

This paper describes SYSTRAN submissions to the WMT 2021 terminology shared task. We participate in the English-to-French translation direction with a standard Transformer neural machine translation network that we enhance with the ability to dynamically include terminology constraints, a very common industrial practice. Two state-of-the-art terminology insertion methods are evaluated based (i) on the use … Fortsetzung

MinhQuang Pham, Antoine Senellart, Dan Berrebbi, Josep Crego, Jean Senellart

Proceedings of the Sixth Conference on Machine Translation (WMT), Online, November 10-11, 2021

Revisiting Multi-Domain Machine Translation

Revisiting Multi-Domain Machine Translation

When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit … Fortsetzung

MinhQuang Pham, Josep Maria Crego, François Yvon

Transactions of the Association for Computational Linguistics 9: 17–35, February 1th, 2021

Integrating Domain Terminology into Neural Machine Translation

Integrating Domain Terminology into Neural Machine Translation

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 … Fortsetzung

Elise Michon, Josep Maria Crego, Jean Senellart

Proceedings of the 28th International Conference on Computational Linguistics, December 2020

A Study of Residual Adapters for Multi-Domain Neural Machine Translation

A Study of Residual Adapters for Multi-Domain Neural 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 … Fortsetzung

MinhQuang Pham, Josep Maria Crego, François Yvon, Jean Senellart

Proceedings of the Fifth Conference on Machine Translation, November 2020

Priming Neural Machine Translation

Priming Neural 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 … Fortsetzung

MinhQuang Pham, Jitao Xu, Josep Maria Crego, François Yvon, Jean Senellart

Proceedings of the Fifth Conference on Machine Translation,November 2020

Efficient and High-Quality Neural Machine Translation with OpenNMT

Efficient and High-Quality Neural Machine Translation with OpenNMT

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 … Fortsetzung

Guillaume Klein, Dakun Zhang, Clément Chouteau, Josep Crego, Jean Senellart

Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 211--217, Association for Computational Linguistics, July 2020

Boosting Neural Machine Translation with Similar Translations

Boosting Neural Machine Translation with Similar Translations

This presentation demonstrates data augmentation methods for Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. We show how we simply feed the neural model with information on both source and target sides of the fuzzy matches, and we also extend the similarity to include … Fortsetzung

Jitao Xu, Josep Crego, Jean Senellart

Proceedings of the Sixth Conference on Machine Translation (WMT), Online, November 10-11, 2021

Generic and Specialized Word Embeddings for Multi-Domain Machine Translation

Generic and Specialized Word Embeddings for Multi-Domain Machine Translation

Supervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case, adaptation techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting, multi-domain adaptation, where the number of domains is potentially large and … Fortsetzung

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

Book: "International Workshop on Spoken Language Translation", "Proceedings of the 16th International Workshop on Spoken Language Translation (IWSLT)", November 2019, Hong-Kong, China