Context-aware Machine Translation aims to improve translations of sentences
by incorporating surrounding sentences as context. Towards this task, two main
architectures have been applied, namely single-encoder (based on concatenation)
and multi-encoder models. In this study, we show that a special case of
multi-encoder architecture, where the latent representation of the source
sentence is cached and reused as the context in the next step, achieves higher
accuracy on the contrastive datasets (where the models have to rank the correct
translation among the provided sentences) and comparable BLEU and COMET scores
as the single- and multi-encoder approaches. Furthermore, we investigate the
application of Sequence Shortening to the cached representations. We test three
pooling-based shortening techniques and introduce two novel methods - Latent
Grouping and Latent Selecting, where the network learns to group tokens or
selects the tokens to be cached as context. Our experiments show that the two
methods achieve competitive BLEU and COMET scores and accuracies on the
contrastive datasets to the other tested methods while potentially allowing for
higher interpretability and reducing the growth of memory requirements with
increased context size.

在此研究中，我们展示了一种特殊情况的多编码器架构，其中源句子的潜在表示被缓存并在下一步中被重复使用作为上下文，这在对比数据集上实现了更高的准确度，并且与单编码器和多编码器方法相比具有相可比的 BLEU 和 COMET 分数。此外，我们还研究了将序列缩短应用于缓存表示的方法，我们测试了三种基于汇聚的缩短技术，并引入了两种新方法 - 潜在分组和潜在选择，其中网络学习将标记分组或选择为上下文进行缓存。我们的实验结果表明，这两种方法在对比数据集上达到了与其他测试方法相当的 BLEU 和 COMET 分数和准确度，同时可能具有更高的可解释性，并在上下文大小增加时减少了内存需求的增长。