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Found 31 papers, 4 papers with code
Analyzing Knowledge Distillation in Neural Machine Translation
Knowledge distillation has recently been successfully applied to neural machine translation.
SYSTRAN @ WMT 2021: Terminology Task
This paper describes SYSTRAN submissions to the WMT 2021 terminology shared task.
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.
Lexical Micro-adaptation for Neural Machine Translation
This work is inspired by a typical machine translation industry scenario in which translators make use of in-domain data for facilitating translation of similar or repeating sentences.
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
Domain adaptation is an old and vexing problem for machine translation systems.
Robust Translation of French Live Speech Transcripts
Despite a narrowed performance gap with direct approaches, cascade solutions, involving automatic speech recognition (ASR) and machine translation (MT) are still largely employed in speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
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.
Efficient and High-Quality Neural Machine Translation with OpenNMT
This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task.
Boosting Neural Machine Translation with Similar Translations
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.
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.
SYSTRAN @ WNGT 2019: DGT Task
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).
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task
This paper describes Systran{'}s submissions to WAT 2019 Russian-Japanese News Commentary task.
Fixing Translation Divergences in Parallel Corpora for Neural MT
Corpus-based approaches to machine translation rely on the availability of clean parallel corpora.
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).
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.
Automatic Speech Recognition (ASR)
Dialect Identification
+3
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.
OpenNMT: Neural Machine Translation Toolkit
OpenNMT is an open-source toolkit for neural machine translation (NMT).
SYSTRAN Purely Neural MT Engines for WMT2017
This paper describes SYSTRAN's systems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions.
OpenNMT: Open-source Toolkit for Neural Machine Translation
We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintaining competitive performance, modularity and reasonable training requirements.
Conception d'une solution de d\'etection d'\'ev\'enements bas\'ee sur Twitter (Design of a solution for event detection from Tweeter)
Cet article pr{\'e}sente un syst{\`e}me d{'}alertes fond{\'e} sur la masse de donn{\'e}es issues de Tweeter.
Adaptation incr\'ementale de mod\`eles de traduction neuronaux (Incremental adaptation of neural machine translation models)
L{'}adaptation au domaine est un verrou scientifique en traduction automatique.
OpenNMT: Open-Source Toolkit for Neural Machine Translation
We describe an open-source toolkit for neural machine translation (NMT).
Neural Machine Translation from Simplified Translations
We conduct preliminary experiments showing that translation complexity is actually reduced in a translation of a source bi-text compared to the target reference of the bi-text while using a neural machine translation (NMT) system learned on the exact same bi-text.
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Domain adaptation is a key feature in Machine Translation.
Boosting Neural Machine Translation
Training efficiency is one of the main problems for Neural Machine Translation (NMT).
Domain Control for Neural Machine Translation
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.
SYSTRAN's Pure Neural Machine Translation Systems
Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existing technologies.
