  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 https://github.com/NJUNLP/knn-box.
