Context-aware Neural Machine Translation for English-Japanese Business
  Scene Dialogues
Sumire Honda Patrick Fernandes Chrysoula Zerva
Despite the remarkable advancements in machine translation, the current
sentence-level paradigm faces challenges when dealing with highly-contextual
languages like Japanese. In this paper, we explore how context-awareness can
improve the performance of the current Neural Machine Translation (NMT) models
for English-Japanese business dialogues translation, and what kind of context
provides meaningful information to improve translation. As business dialogue
involves complex discourse phenomena but offers scarce training resources, we
adapted a pretrained mBART model, finetuning on multi-sentence dialogue data,
which allows us to experiment with different contexts. We investigate the
impact of larger context sizes and propose novel context tokens encoding
extra-sentential information, such as speaker turn and scene type. We make use
of Conditional Cross-Mutual Information (CXMI) to explore how much of the
context the model uses and generalise CXMI to study the impact of the
extra-sentential context. Overall, we find that models leverage both preceding
sentences and extra-sentential context (with CXMI increasing with context size)
and we provide a more focused analysis on honorifics translation. Regarding
translation quality, increased source-side context paired with scene and
speaker information improves the model performance compared to previous work
and our context-agnostic baselines, measured in BLEU and COMET metrics.