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Author
Last Commit
Oct. 20, 2018
Created
Sep. 13, 2018

Overview

This repository contains PyTorch implementations of sequence to sequence models for machine translation. The code is based on fairseq and purportedly made simple for the sake of readability, although main features such as multi-GPU training and beam search remain intact. The implemented model is a classic LSTM-based encoder-decoder model with attention mechanism, which performs robustly well on several machine translation datasets.

drawing

Installation

The code was written for Python 3.6 or higher, and it has been tested with PyTorch 0.4.1. Training is only available with GPU. To get started, try to clone the repository

git clone https://github.com/tangbinh/machine-translation
cd machine-translation

Preprocessing

To download the IWSLT'14 DE-EN dataset and perform tokenization, it might be easier to just run:

bash download.sh

Then, the following commands help build dictionaries and map tokens into indices:

DATA_PATH=data/iwslt14.tokenized.de-en
python preprocess.py --source-lang de --target-lang en --train-prefix $DATA_PATH/train --valid-prefix $DATA_PATH/valid --test-prefix $DATA_PATH/test --dest-dir data-bin/iwslt14.tokenized.de-en

Training

To get started with training a model on SQuAD, you might find the following commands helpful:

python train.py --data data-bin/iwslt14.tokenized.de-en --source-lang de --target-lang en --lr 0.25 --clip-norm 0.1 --max-tokens 12000 --save-dir checkpoints/lstm

Prediction

When the training is done, you can make predictions and compute BLEU scores:

python generate.py --data data-bin/iwslt14.tokenized.de-en --checkpoint-path checkpoints/lstm/checkpoint_best.pt > /tmp/lstm.out
grep ^H /tmp/lstm.out | cut -f2- | sed -r 's/'$(echo -e "\033")'\[[0-9]{1,2}(;([0-9]{1,2})?)?[mK]//g' > /tmp/lstm.sys
grep ^T /tmp/lstm.out | cut -f2- | sed -r 's/'$(echo -e "\033")'\[[0-9]{1,2}(;([0-9]{1,2})?)?[mK]//g' > /tmp/lstm.ref
python score.py --reference /tmp/lstm.ref --system /tmp/lstm.sys