Machine translation (MT) is an important task in natural language processing (NLP), as it
automates the translation process and reduces the reliance on human translators. With the …

Despite the progress made in sentence-level NMT, current systems still fall short at
achieving fluent, good quality translation for a full document. Recent works in context-aware …

We present a document-level neural machine translation model which takes both source
and target document context into account using memory networks. We model the problem as …

Recently, there has been a growing interest in designing text generation systems from a
discourse coherence perspective, eg, modeling the interdependence between sentences …

Standard automatic metrics, eg BLEU, are not reliable for document-level MT evaluation.
They can neither distinguish document-level improvements in translation quality from …

We hypothesize that existing sentence-level machine translation (MT) metrics become less
effective when the human reference contains ambiguities. To verify this hypothesis, we …

As the quality of Machine Translation (MT) improves, research on improving discourse in
automatic translations becomes more viable. This has resulted in an increase in the amount …

Abstract Machine translation (MT) is an important task in natural language processing (NLP)
as it automates the translation process and reduces the reliance on human translators. With …

This paper introduces the problem of measuring coherence in Machine Translation.
Previously, local coherence has been assessed in a monolingual context using essentially …

It is well known that translations generated by an excellent document-level neural machine
translation (NMT) model are consistent and coherent. However, existing sentence-level …