116 citations · 237 across the 6 of their papers we have counts for
6 papers · 1 filter
On the cross-lingual transferability of multilingual prototypical models across NLU tasks
Oralie Cattan, Christophe Servan, Sophie Rosset
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven to be effective for limited domain and language applications when a sufficient n…
Benchmarking Transformers-based models on French Spoken Language Understanding tasks
Oralie Cattan, Sahar Ghannay, Christophe Servan +1
In the last five years, the rise of the self-attentional Transformer-based architectures led to state-of-the-art performances over many natural language tasks. Although these appro…
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Christophe Servan, Josep Crego, Jean Senellart
Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Compute…
Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation
Alexandre Berard, Olivier Pietquin, Christophe Servan +1
This paper proposes a first attempt to build an end-to-end speech-to-text translation system, which does not use source language transcription during learning or decoding. We propo…
SYSTRAN's Pure Neural Machine Translation Systems
Josep Crego, Jungi Kim, Guillaume Klein +27
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 severa…
Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
Christophe Servan, Alexandre Berard, Zied Elloumi +2
This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is ma…