Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has
recently emerged as the first technology able to challenge the long-standing dominance of …

Neural machine translation (NMT) is a deep learning based approach for machine
translation, which yields the state-of-the-art translation performance in scenarios where …

Warning: This paper contains explicit statements of offensive stereotypes which may be
upsetting. Much work on biases in natural language processing has addressed biases …

We introduce a curriculum learning approach to adapt generic neural machine translation
models to a specific domain. Samples are grouped by their similarities to the domain of …

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 …

To better understand the effectiveness of continued training, we analyze the major
components of a neural machine translation system (the encoder, decoder, and each …

Within the field of statistical machine translation, the neural approach (NMT) is currently
pushing ahead the state of the art performance traditionally achieved by phrase-based …

We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on
Morphological Reinflection. This task involves natural language generation: systems are …

Neural machine translation (NMT) is a deep learning based approach for machine
translation, which outperforms traditional statistical machine translation (SMT) and yields the …

Abstract Domain adaptation is an active field of research in statistical machine translation
(SMT), but so far most work has ignored the distinction between the topic and genre of …