Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

Last update: Nov 24, 2022

Related tags

Overview

KSTER

Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper].

Usage

Download the processed datasets from this site. You can also download the built databases from this site and download the model checkpoints from this site.

Train a general-domain base model

Take English -> Germain translation for example.

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m joeynmt train configs/transformer_base_wmt14_en2de.yaml

Finetuning trained base model on domain-specific datasets

Take English -> Germain translation in Koran domain for example.

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m joeynmt train configs/transformer_base_koran_en2de.yaml

Build database

Take English -> Germain translation in Koran domain for example, wmt14_en_de.transformer.ckpt is the path of trained general-domain base model checkpoint.

mkdir database/koran_en_de_base
export CUDA_VISIBLE_DEVICES=0
python3 -m joeynmt build_database configs/transformer_base_koran_en2de.yaml \
        --ckpt wmt14_en_de.transformer.ckpt \
        --division train \
        --index_path database/koran_en_de_base/trained.index \
        --token_map_path database/koran_en_de_base/token_map \
        --embedding_path database/koran_en_de_base/embeddings.npy

Train the bandwidth estimator and weight estimator in KSTER

Take English -> Germain translation in Koran domain for example.

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m joeynmt combiner_train configs/transformer_base_koran_en2de.yaml \
        --ckpt wmt14_en_de.transformer.ckpt \
        --combiner dynamic_combiner \
        --top_k 16 \
        --kernel laplacian \
        --index_path database/koran_en_de_base/trained.index \
        --token_map_path database/koran_en_de_base/token_map \
        --embedding_path database/koran_en_de_base/embeddings.npy \
        --in_memory True

Inference

We unify the inference of base model, finetuned or joint-trained model, kNN-MT and KSTER with a concept of combiner (see joeynmt/combiners.py).

Combiner type	Methods	Description
NoCombiner	Base, Finetuning, Joint-training	Directly inference without retrieval.
StaticCombiner	kNN-MT	Retrieve similar examples during inference. mixing_weight and bandwidth are pre-specified.
DynamicCombiner	KSTER	Retrieve similar examples during inference. mixing_weight and bandwidth are dynamically estimated.

Inference with NoCombiner for Base model

Take English -> Germain translation in Koran domain for example.

export CUDA_VISIBLE_DEVICES=0
python3 -m joeynmt test configs/transformer_base_koran_en2de.yaml \
        --ckpt wmt14_en_de.transformer.ckpt \
        --combiner no_combiner

Inference with StaticCombiner for kNN-MT

Take English -> Germain translation in Koran domain for example.

export CUDA_VISIBLE_DEVICES=0
python3 -m joeynmt test configs/transformer_base_koran_en2de.yaml \
        --ckpt wmt14_en_de.transformer.ckpt \
        --combiner static_combiner \
        --top_k 16 \
        --mixing_weight 0.7 \
        --bandwidth 10 \
        --kernel gaussian \
        --index_path database/koran_en_de_base/trained.index \
        --token_map_path database/koran_en_de_base/token_map

Inference with DynamicCombiner for KSTER

Take English -> Germain translation in Koran domain for example, koran_en_de.laplacian.combiner.ckpt is the path of trained bandwidth estimator and weight estimator for Koran domain.
--in_memory option specifies whether to load the example embeddings to memory. Set in_memory == True for faster inference, set in_memory == False for lower memory demand.

export CUDA_VISIBLE_DEVICES=0
python3 -m joeynmt test configs/transformer_base_koran_en2de.yaml \
        --ckpt wmt14_en_de.transformer.ckpt \
        --combiner dynamic_combiner \
        --combiner_path koran_en_de.laplacian.combiner.ckpt \
        --top_k 16 \
        --kernel laplacian \
        --index_path database/koran_en_de_base/trained.index \
        --token_map_path database/koran_en_de_base/token_map \
        --embedding_path database/koran_en_de_base/embeddings.npy \
        --in_memory True

See bash_scripts/test_*.sh for reproducing our results.
See logs/*.log for the logs of our results.

Acknowledgements

We build the models based on the joeynmt codebase.

Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

Related tags

Overview

KSTER

Usage

Train a general-domain base model

Finetuning trained base model on domain-specific datasets

Build database

Train the bandwidth estimator and weight estimator in KSTER

Inference

Inference with NoCombiner for Base model

Inference with StaticCombiner for kNN-MT

Inference with DynamicCombiner for KSTER

Acknowledgements

Owner

jiangqn

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations" [arXiv 2022].

Baselines for TrajNet++

Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Predicting 10 different clothing types using Xception pre-trained model.

MolRep: A Deep Representation Learning Library for Molecular Property Prediction

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

Connecting Java/ImgLib2 + Python/NumPy

This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".

EPSANet：An Efficient Pyramid Split Attention Block on Convolutional Neural Network

Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Face and Body Tracking for VRM 3D models on the web.

This is an official implementation for "PlaneRecNet".

Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning

PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

💡 Type hints for Numpy

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Balancing Principle for Unsupervised Domain Adaptation

Automatic Differentiation Multipole Moment Molecular Forcefield

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.