Code for our AAAI 2022 paper "Non-Parametric Online Learning from Human Feedback for Neural Machine Translation".

This project implements our KoK(KNN-Over-KNN) as well as Vanilla KNN-MT. The implementation is build upon Adaptive kNN-MT, many thanks to the authors for making their code avaliable.

Requirements and Installation

• pytorch version >= 1.5.0
• python version >= 3.6
• faiss-gpu >= 1.6.5
• pytorch_scatter = 2.0.5
• 1.19.0 <= numpy < 1.20.0

You can install this project by

pip install --editable ./

Instructions

We use an example to show how to use our codes.

Pre-trained Model and Data

The pre-trained translation model can be downloaded from this site. We use the De->En Single Model for all experiments.

For convenience, We provide pre-processed EMEA data and JRC-Acquis Dataset.

Inference with KoK

export CUDA_VISIBLE_DEVICES=$1

DATA_PATH=''
MODEL_PATH=''
OUTPUT_PATH=''
split=train

python fairseq_cli/knn_generate.py $DATA_PATH \
  --gen-subset $split \
  --path $MODEL_PATH --arch knn_transformer_wmt19 \
  --beam 4 --lenpen 0.6 --max-len-a 1.2 --max-len-b 10 --source-lang de --target-lang en \
  --scoring sacrebleu \
  --batch-size 1 \
  --tokenizer moses --remove-bpe \
  --results-path $OUTPUT_PATH \
  --model-overrides "{'k': 8, 'lambda_value': 0.3, 'temperature_value': 10, 'label_temperature_value': 10,
  'arch': 'knn_transformer_wmt19', 'knn_type': 'label-datastore', 'distance_threshold': 100
  }"

We recommend you to use the below command:

bash scripts/ema/all_kok.sh

Inference with KNN-MT

We also provide scripts to do vanilla kNN-MT inference

DATA_PATH=''
MODEL_PATH=''
OUTPUT_PATH='
split=train

python fairseq_cli/knn_generate.py $DATA_PATH \
  --gen-subset $split \
  --path $MODEL_PATH --arch knn_transformer_wmt19 \
  --beam 4 --lenpen 0.6 --max-len-a 1.2 --max-len-b 10 --source-lang de --target-lang en \
  --scoring sacrebleu \
  --batch-size 1 \
  --tokenizer moses --remove-bpe \
  --results-path $OUTPUT_PATH \
  --model-overrides "{'k': 8, 'lambda_value': 0.2, 'temperature_value': 10, 'arch': 'knn_transformer_wmt19', \
  'knn_type': 'normal' }"

We recommend you to use below command:

bash scripts/ema/all_knn.sh 1 c