Papers and publications

Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation

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

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 … Continued

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 … Continued

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

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 … Continued

MinhQuang Pham, Josep Maria Crego, François Yvon

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 … Continued

Elise Michon, Josep Maria Crego, Jean Senellart

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 … Continued

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

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 … Continued

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

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 … Continued

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

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 … Continued

Jitao Xu, Josep Crego, Jean Senellart

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 … Continued

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