This is the code base for paper "Countering Language Drift with Iterated Learning". Please cite the following if you found this codebase to be useful
@article{lu2020countering,
title={Countering language drift with seeded iterated learning},
author={Lu, Yuchen and Singhal, Soumye and Strub, Florian and Pietquin, Olivier and Courville, Aaron},
journal={arXiv preprint arXiv:2003.12694},
year={2020}
}
This codebase is tested on python3.7 and torch==1.3.1.
Clone this repo, and at project root do pip install -e . as well as other dependencies.
python preprocess/prepare_text.py -data_dir DATA_DIR
Put the images of multi30k under the directory DATA_DIR/multi30k/imgs,
then do
python preprocess/extract_flickr30_imgfeats.py -datadir DATA_DIR
This will produce train_feat.pth and val_feat.pth under DATA_DIR/flickr30k.
python run_pretrain.py --config JSON_PATH --data_dir DATA_DIR --exp_dir EXP_ROOT_DIR
python run_finetune.py --config JSON_PATH --data_dir DATA_DIR --exp_dir EXP_ROOT_DIR
Sample json config can be found under the folder jsons.
iwslt_en_de.json,iwslt_fr_en.json: Config for pretraining translation agents.hyperparames_caption.json: Config for language model used for scoring generated English.hyperparames_ranker.json: Config for pretraining image retriever.gumbel/: Configs for Gumbel finetuning (vanilla Gumbel, SIL, S2P)