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.

通过研究上下文感知如何改善当前神经机器翻译模型在英日商务对话翻译中的表现，并探索哪种上下文对于提升翻译质量提供有意义的信息，本文使用事先训练的 mBART 模型在多句对话数据上进行微调，研究了更大上下文范围的影响，并提出了编码额外片段信息（如发言人和场景类型）的新型上下文标记，实验结果发现模型利用前几个句子和额外片段的上下文（随着上下文范围的增加，CXMI 也在增加）以及对敬语翻译进行了更专注的分析，而在翻译质量方面，与以往的工作和无上下文的基线比较，增加源语言上下文信息和场景、发言人信息有助于提高模型性能（使用 BLEU 和 COMET 指标进行衡量）。