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

jiangqn

Last update: Nov 24, 2022

Related tags

Deep Learning KSTER

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.

This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

35 Nov 17, 2022

Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".

Interactive Machine Comprehension with Dynamic Knowledge Graphs Implementation for the EMNLP 2021 paper. Dependencies apt-get -y update apt-get instal

19 Aug 23, 2022

This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.

STaCK: Sentence Ordering with Temporal Commonsense Knowledge This repository contains the pytorch implementation of the paper STaCK: Sentence Ordering

Deep Cognition and Language Research (DeCLaRe) Lab

23 Dec 16, 2022

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

Learning Opinion Summarizers by Selecting Informative Reviews This repository contains the codebase and the dataset for the corresponding EMNLP 2021

39 Jan 1, 2023

Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

57 Jan 1, 2023

EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at [email protected]

9 Oct 28, 2022

PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

34 Dec 21, 2022

Code and data for "Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning" (EMNLP 2021).

GD-VCR Code for Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning (EMNLP 2021). Research Questions and Aims: How well can a model perform o

24 Oct 13, 2022

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

16 Jul 16, 2022

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

You might also like...

This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".

This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Code and data for "Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning" (EMNLP 2021).

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Owner

jiangqn

Related resources for our EMNLP 2021 paper

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Coming soon!

The code repository for EMNLP 2021 paper "Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization".

This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis, accepted at EMNLP 2021.

Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

This repo is the code release of EMNLP 2021 conference paper "Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories".