Retrieval-Augmented Neural Machine Translation (RAMT) architectures retrieve
examples from memory to guide the generation process. While most works in this
trend explore new ways to exploit the retrieved examples, the upstream
retrieval step is mostly unexplored. In this paper, we study the effect of
varying retrieval methods for several translation architectures, to better
understand the interplay between these two processes. We conduct experiments in
two language pairs in a multi-domain setting and consider several downstream
architectures based on a standard autoregressive model, an edit-based model,
and a large language model with in-context learning. Our experiments show that
the choice of the retrieval technique impacts the translation scores, with
variance across architectures. We also discuss the effects of increasing the
number and diversity of examples, which are mostly positive across the board.

通过实验研究了不同的检索方法对几种翻译体系结构的影响，以更好地理解检索和生成两个过程之间的相互作用。研究表明，检索技术的选择会影响翻译结果，且不同体系结构之间存在方差。此外，研究还探讨了增加示例数量和多样性的效果，普遍上是积极的。

检索增强的神经机器翻译中的从内存中检索示例：系统性比较

Retrieving Examples from Memory for Retrieval Augmented Neural Machine  Translation: A Systematic Comparison

Retrieval-Augmented Machine Translation (RAMT) is attracting growing
attention. This is because RAMT not only improves translation metrics, but is
also assumed to implement some form of domain adaptation. In this contribution,
we study another salient trait of RAMT, its ability to make translation
decisions more transparent by allowing users to go back to examples that
contributed to these decisions.
For this, we propose a novel architecture aiming to increase this
transparency. This model adapts a retrieval-augmented version of the
Levenshtein Transformer and makes it amenable to simultaneously edit multiple
fuzzy matches found in memory. We discuss how to perform training and inference
in this model, based on multi-way alignment algorithms and imitation learning.
Our experiments show that editing several examples positively impacts
translation scores, notably increasing the number of target spans that are
copied from existing instances.

提出了一种新颖的基于检索增强的 Levenshtein Transformer 架构，旨在提高 Retrieval-Augmented Machine Translation 的透明度，并通过编辑多个模糊匹配的示例来改善翻译得分。