📝 INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation

Code for our ACL 2023 paper "INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation". Our code is highly inspired by Adaptive kNN-MT. More details and guidance can be found in this repository: https://github.com/zhengxxn/adaptive-knn-mt.

Requirements and Installation

• python >= 3.7
• pytorch >= 1.10.0
• faiss-gpu >= 1.7.3
• sacremoses == 0.0.41
• sacrebleu == 1.5.1
• fastBPE == 0.1.0

You can install this repository by

git clone [email protected]:OwenNJU/INK.git
cd INK 
pip install --editable ./

Note: Installing faiss with pip is not suggested. For stability, we recommand you to install faiss with conda

CPU version only:
conda install faiss-cpu -c pytorch

GPU version:
conda install faiss-gpu -c pytorch # For CUDA

Base Model and Data

We use the winner model of WMT'19 German-English news translation tasks as the off-the-shelf NMT model in our experiments, which can be downloaded from this site.

We conduct experiments on four benchmark OPUS dataset. We directly use the preprocessed data released by Zheng et al., which can be downloaded from this site.

Scripts

Below we provide scripts to run INK system:

# training 
bash ./run_scripts/train.ink.sh

# inference
bash ./run_scripts/inference.ink.sh

Citation

If you find this repository helpful, feel free to cite our paper:

@inproceedings{zhu2023ink,
    title = "INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation",
    author = "Zhu, Wenhao  and
      Xu, Jingjing  and
      Huang, Shujian  and
      Kong, Lingpeng  and
      Chen, Jiajun",
    booktitle = "Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)",
    year = "2023",
}