The advent of context-aware NMT has resulted in promising improvements in the
overall translation quality and specifically in the translation of discourse
phenomena such as pronouns. Previous works have mainly focused on the use of
past sentences as context with a focus on anaphora translation. In this work,
we investigate the effect of future sentences as context by comparing the
performance of a contextual NMT model trained with the future context to the
one trained with the past context. Our experiments and evaluation, using
generic and pronoun-focused automatic metrics, show that the use of future
context not only achieves significant improvements over the context-agnostic
Transformer, but also demonstrates comparable and in some cases improved
performance over its counterpart trained on past context. We also perform an
evaluation on a targeted cataphora test suite and report significant gains over
the context-agnostic Transformer in terms of BLEU.

本研究探讨了将未来句子作为上下文与使用过去句子作为上下文的翻译模型效果，结果表明使用未来句子作为上下文不仅可以显著改善翻译效果，而且在某些情況下比过去句子更有效。同时，我们还在特定情况下的测试中获得了显著的改善。

上下文神经机器翻译提高了指代前置代词的翻译

Contextual Neural Machine Translation Improves Translation of Cataphoric  Pronouns

Though machine translation errors caused by the lack of context beyond one
sentence have long been acknowledged, the development of context-aware NMT
systems is hampered by several problems. Firstly, standard metrics are not
sensitive to improvements in consistency in document-level translations.
Secondly, previous work on context-aware NMT assumed that the sentence-aligned
parallel data consisted of complete documents while in most practical scenarios
such document-level data constitutes only a fraction of the available parallel
data. To address the first issue, we perform a human study on an
English-Russian subtitles dataset and identify deixis, ellipsis and lexical
cohesion as three main sources of inconsistency. We then create test sets
targeting these phenomena. To address the second shortcoming, we consider a
set-up in which a much larger amount of sentence-level data is available
compared to that aligned at the document level. We introduce a model that is
suitable for this scenario and demonstrate major gains over a context-agnostic
baseline on our new benchmarks without sacrificing performance as measured with
BLEU.

本文通过人类实验和模型构建等研究方法，提出了一种适用于大规模句子级平行数据的上下文感知 NMT 模型，通过解决上下文因素引发的翻译不一致问题，取得了 BLEU 上的显著性能提升。

上下文感知机器翻译在指代、省略和词汇凝聚方面的改进

When a Good Translation is Wrong in Context: Context-Aware Machine  Translation Improves on Deixis, Ellipsis, and Lexical Cohesion

Despite the progress made in sentence-level NMT, current systems still fall
short at achieving fluent, good quality translation for a full document. Recent
works in context-aware NMT consider only a few previous sentences as context
and may not scale to entire documents. To this end, we propose a novel and
scalable top-down approach to hierarchical attention for context-aware NMT
which uses sparse attention to selectively focus on relevant sentences in the
document context and then attends to key words in those sentences. We also
propose single-level attention approaches based on sentence or word-level
information in the context. The document-level context representation, produced
from these attention modules, is integrated into the encoder or decoder of the
Transformer model depending on whether we use monolingual or bilingual context.
Our experiments and evaluation on English-German datasets in different document
MT settings show that our selective attention approach not only significantly
outperforms context-agnostic baselines but also surpasses context-aware
baselines in most cases.

提出了基于上下文感知 NMT 的层次化注意力机制来处理整个文档的翻译，并使用稀疏注意力机制在文档上选择性地关注相关句子和关键词，将这些注意力模块产生的文档级上下文表示集成到 Transformer 模型的编码器或解码器中，通过在英德数据集上的实验进一步验证了该方法的有效性。