Document Level Machine Translation
7 papers with code • 1 benchmarks • 0 datasets
In this paper, we present a proof-of-concept implementation of a coreference-aware decoder for document-level machine translation.
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.
Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted.
Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context -- context from sentences other than those currently being translated.
However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.
We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level -- which is particularly problematic as systems are typically compared in this manner.