$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing
attention due to its ability to non-parametrically adapt to new translation
domains. By using an upstream NMT model to traverse the downstream training
corpus, it is equipped with a datastore containing vectorized key-value pairs,
which are retrieved during inference to benefit translation. However, there
often exists a significant gap between upstream and downstream domains, which
hurts the retrieval accuracy and the final translation quality. To deal with
this issue, we propose a novel approach to boost the datastore retrieval of
$k$NN-MT by reconstructing the original datastore. Concretely, we design a
reviser to revise the key representations, making them better fit for the
downstream domain. The reviser is trained using the collected
semantically-related key-queries pairs, and optimized by two proposed losses:
one is the key-queries semantic distance ensuring each revised key
representation is semantically related to its corresponding queries, and the
other is an L2-norm loss encouraging revised key representations to effectively
retain the knowledge learned by the upstream NMT model. Extensive experiments
on domain adaptation tasks demonstrate that our method can effectively boost
the datastore retrieval and translation quality of $k$NN-MT.\footnote{Our code
is available at https://github.com/DeepLearnXMU/RevisedKey-knn-mt.}

本研究提出了一种新颖的方法来增强 $k$NN-MT 的数据存储器的检索能力，通过重构原始数据存储器来解决上游和下游域之间存在的显著差距。该方法设计了一个修订者来修订关键表示，使其更适合下游域。修订者使用收集的语义相关键 - 查询对进行训练，并通过两个提出的损失进行优化：一个是关键查询语义距离，确保每个修订后的关键表示与其相应查询有语义相关性 ， 另一个是 L2 - 规范损失，鼓励修订关键表示有效保留上游 NMT 模型所学的知识。在域自适应任务上的大量实验表明，我们的方法可以有效提高 $k$NN-MT 的数据存储器检索和翻译质量。

填补上下文表示中的域间差距，用于 k 近邻神经机器翻译

Bridging the Domain Gaps in Context Representations for k-Nearest  Neighbor Neural Machine Translation

Recently, deep reinforcement learning (DRL) frameworks have shown potential
for solving NP-hard routing problems such as the traveling salesman problem
(TSP) without problem-specific expert knowledge. Although DRL can be used to
solve complex problems, DRL frameworks still struggle to compete with
state-of-the-art heuristics showing a substantial performance gap. This paper
proposes a novel hierarchical problem-solving strategy, termed learning
collaborative policies (LCP), which can effectively find the near-optimum
solution using two iterative DRL policies: the seeder and reviser. The seeder
generates as diversified candidate solutions as possible (seeds) while being
dedicated to exploring over the full combinatorial action space (i.e., sequence
of assignment action). To this end, we train the seeder's policy using a simple
yet effective entropy regularization reward to encourage the seeder to find
diverse solutions. On the other hand, the reviser modifies each candidate
solution generated by the seeder; it partitions the full trajectory into
sub-tours and simultaneously revises each sub-tour to minimize its traveling
distance. Thus, the reviser is trained to improve the candidate solution's
quality, focusing on the reduced solution space (which is beneficial for
exploitation). Extensive experiments demonstrate that the proposed two-policies
collaboration scheme improves over single-policy DRL framework on various
NP-hard routing problems, including TSP, prize collecting TSP (PCTSP), and
capacitated vehicle routing problem (CVRP).

本论文提出了一种新的层次化问题求解策略，称为学习协作策略，它使用两个迭代的深度强化学习策略：播种机和修订机，旨在有效地找到近似最优解，扩展了深度强化学习框架在解决 NP-hard 路由问题方面的应用。