/CMLMC

Code for the ICLR'22 paper "Improving Non-Autoregressive Translation Models Without Distillation"

Primary LanguagePythonMIT LicenseMIT

ICLR'22 Improving Non-Autoregressive Translation Models Without Distillation

[paper]

Authors: Xiao Shi Huang, Felipe Perez, Maksims Volkovs

Introduction

This repository contains a full implementation of the CMLMC implemented with the fairseq library, and includes both training and evaluation routines on the IWSLT'14 De-En dataset.

Environment

The python code is developed and tested on the following environment:

  • Python 3.7.9
  • Pytorch 1.10.0

Experiments on IWSLT'14 De-En and En-De datasets (included in this repo) were run on NVIDIA V100 GPU with 32GB GPU memory; all other experiments were run on an IBM server with 160 POWER9 CPUs, 600GB RAM and 4 Tesla V100 GPUs

Dataset

The IWSLT'14 De-En and En-De dataset were included in this repo; for the WMT'14 En-De and WMT'16 En-Ro datasets refer to the fairseq's instructions here

Running The Code

  1. ./trainNAT.sh will train and evaluate both the CMLM benchmark and the CMLMC model on IWSLT'14 De-En raw dataset.
  2. (Optionally) launch tensorboard to monitor progress by tensorboard --logdir=<log_path>

This script runs the 512-1024-4 Transformer NAR model (see paper for details). By default all avialable GPUs are used, but parameters such as batchsize are set for for 1 GPU. If multiple GPUs are avaialbe, either point the script to only one GPU or adjust model parameters accordingly.