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

Bounding Wasserstein distance with couplings

Notification Triggers for Python

PCGNN - Procedural Content Generation with NEAT and Novelty

GANfolk: Using AI to create portraits of fictional people to sell as NFTs

Implementation of CVPR 2020 Dual Super-Resolution Learning for Semantic Segmentation

IhoneyBakFileScan Modify - 批量网站备份文件扫描器，增加文件规则，优化内存占用

Multi-Joint dynamics with Contact. A general purpose physics simulator.

A numpy-based implementation of RANSAC for fundamental matrix and homography estimation. The degeneracy updating and local optimization components are included and optional.

Classification of ecg datas for disease detection

Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

Official PyTorch implementation of the paper "Self-Supervised Relational Reasoning for Representation Learning", NeurIPS 2020 Spotlight.

Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

PyTorch implementation of Barlow Twins.

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

Curated list of awesome GAN applications and demo

OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021)