IWSLT 2017
Related Papers
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017
AbstractNeural Machine Translation has been shown to enable inference and cross-lingual knowledge transfer across multiple language directions using a single multilingual model. Focusing on this multilingual translation scenario, this work …...
Going beyond zero-shot MT: combining phonological, morphological and semantic factors. The UdS-DFKI System at IWSLT 2017
AbstractThis paper describes the UdS-DFKI participation to the multilingual task of the IWSLT Evaluation 2017. Our approach is based on factored multilingual neural translation systems following the small data and …...
Kyoto University MT System Description for IWSLT 2017
AbstractWe describe here our Machine Translation (MT) model and the results we obtained for the IWSLT 2017 Multilingual Shared Task. Motivated by Zero Shot NMT [1] we trained a Multilingual …...
Overview of the IWSLT 2017 Evaluation Campaign
AbstractThe IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, …...
The RWTH Aachen Machine Translation Systems for IWSLT 2017
AbstractThis work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language …...
Improving Zero-shot Translation with Language-Independent Constraints
AbstractAn important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one …...