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 …

Standard machine translation systems process sentences in isolation and hence ignore
extra-sentential information, even though extended context can both prevent mistakes in …

Neural Machine Translation (NMT) can be improved by including document-level contextual
information. For this purpose, we propose a hierarchical attention model to capture the …

The translation of pronouns presents a special challenge to machine translation to this day,
since it often requires context outside the current sentence. Recent work on models that …

This paper does not aim at introducing a novel model for document-level neural machine
translation. Instead, we head back to the original Transformer model and hope to answer the …

Zero pronouns (ZPs) are frequently omitted in pro-drop languages (eg Chinese, Hungarian,
and Hindi), but should be recalled in non-pro-drop languages (eg English). This …

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

Document-level machine translation (MT) remains challenging due to the difficulty in
efficiently using document context for translation. In this paper, we propose a hierarchical …

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

We evaluate the output of 16 English-to-German MT systems with respect to the translation
of pronouns in the context of the WMT 2018 competition. We work with a test suite …