Despite significant improvements in enhancing the quality of translation,
context-aware machine translation (MT) models underperform in many cases. One
of the main reasons is that they fail to utilize the correct features from
context when the context is too long or their models are overly complex. This
can lead to the explain-away effect, wherein the models only consider features
easier to explain predictions, resulting in inaccurate translations. To address
this issue, we propose a model that explains the decisions made for translation
by predicting coreference features in the input. We construct a model for input
coreference by exploiting contextual features from both the input and
translation output representations on top of an existing MT model. We evaluate
and analyze our method in the WMT document-level translation task of
English-German dataset, the English-Russian dataset, and the multilingual TED
talk dataset, demonstrating an improvement of over 1.0 BLEU score when compared
with other context-aware models.

通过预测输入中的核指代特征，我们提出了一种可以解释翻译决策的模型，通过利用输入和翻译输出表示中的上下文特征，以提高英语 - 德语数据集、英语 - 俄语数据集和多语言 TED 演讲数据集上的文档级翻译任务 BLEU 分数超过 1.0 的效果。