RNNsearch

An implementation of RNNsearch using Theano, the implementation is identical to GroundHog.

Note

This repository is deprecated. See RNNsearch

Usage

Data Preprocessing

1. Build vocabulary

• Build source vocabulary

python scripts/buildvocab.py --corpus zh.txt --output vocab.zh.pkl
                             --limit 30000 --groundhog

• Build target vocabulary

python scripts/buildvocab.py --corpus en.txt --output vocab.en.pkl
                             --limit 30000 --groundhog

2. Shuffle corpus (Optional)

python scripts/shuffle.py --corpus zh.txt en.txt

Training

• Training from random initialization

  python rnnsearch.py train --corpus zh.txt.shuf en.txt.shuf
    --vocab zh.vocab.pkl en.vocab.pkl --model nmt --embdim 620 620
    --hidden 1000 1000 1000 --maxhid 500 --deephid 620 --maxpart 2
    --alpha 5e-4 --norm 1.0 --batch 128 --maxepoch 5 --seed 1234
    --freq 1000 --vfreq 1500 --sfreq 50 --sort 20 --validation nist02.src
    --references nist02.ref0 nist02.ref1 nist02.ref2 nist02.ref3

• Initialize with a trained model

  python rnnsearch.py train --corpus zh.txt.shuf en.txt.shuf
    --vocab zh.vocab.pkl en.vocab.pkl --model nmt --embdim 620 620
    --hidden 1000 1000 1000 --maxhid 500 --deephid 620 --maxpart 2
    --alpha 5e-4 --norm 1.0 --batch 128 --maxepoch 5 --seed 1234
    --freq 1000 --vfreq 1500 --sfreq 50 --sort 20 --validation nist02.src
    --references nist02.ref0 nist02.ref1 nist02.ref2 nist02.ref3
    --initialize nmt.best.pkl

• Resume training

  python rnnsearch.py train --model nmt.autosave.pkl

Decoding

  python rnnsearch.py translate --model nmt.best.pkl < input > translation

Convert Trained Models

Models trained by GroundHog can be converted to our format using convert.py, only support RNNsearch architecture

python scripts/convert.py --state search_state.pkl --model search_model.npz
                          --output nmt.pkl

Convert Old Models

Convert models trained by old versions

python scripts/convert_model.py oldmodel.pkl newmodel.pkl