Augmenting the base neural model with a token-level symbolic datastore is a
novel generation paradigm and has achieved promising results in machine
translation (MT). In this paper, we introduce a unified framework kNN-BOX,
which enables quick development and interactive analysis for this novel
paradigm. kNN-BOX decomposes the datastore-augmentation approach into three
modules: datastore, retriever and combiner, thus putting diverse kNN generation
methods into a unified way. Currently, kNN-BOX has provided implementation of
seven popular kNN-MT variants, covering research from performance enhancement
to efficiency optimization. It is easy for users to reproduce these existing
works or customize their own models. Besides, users can interact with their kNN
generation systems with kNN-BOX to better understand the underlying inference
process in a visualized way. In the experiment section, we apply kNN-BOX for
machine translation and three other seq2seq generation tasks, namely, text
simplification, paraphrase generation and question generation. Experiment
results show that augmenting the base neural model with kNN-BOX leads to a
large performance improvement in all these tasks. The code and document of
kNN-BOX is available at this https URL.

本论文提出了一种新的生成范式，即使用 token-level 符号数据存储器来扩充基础神经模型，已在机器翻译中取得了有优异的成果。我们介绍了一个名为 kNN-BOX 的统一框架，该框架将数据存储增强方法分解为三个模块，并提供了七种流行的 kNN-MT 变体的实现。实验结果表明，使用 kNN-BOX 可以在各种生成任务中显著提高性能。