ysymyth/ec-nl

[ICLR 2022] Linking Emergent and Natural Languages via Corpus Transfer

EC-NL

Code and data for paper Linking Emergent and Natural Languages via Cospus Transfer at ICLR 2022 (spotlight).

@inproceedings{yao2022linking,
  title = {Linking Emergent and Natural Languages via Corpus Transfer},
  author = {Yao, Shunyu and Yu, Mo and Zhang, Yang and Narasimhan, Karthik and Tenenbaum, Joshua and Gan, Chuang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  html = {https://openreview.net/pdf?id=49A1Y6tRhaq},
}

Dependencies

• PyTorch 1.8
• SciPy 1.4
• Transformers 4.4.2
• (Optional) Wandb

Data

Google Drive includes

• image_features: Image features of coco-2014 (coco.pt) and Conceptual Captions (cc.pt) datasets from a pre-trained ResNet, to be used in EC pre-training.

• lm_corpora: Corpora used for language modeling transfer experiments.

Name	Usage	Comment
cc.pt	pre-train	Emergent language
paren-zipf.pt	pre-train	Regular language of nesting parentheses
wiki-es.pt	pre-train	Spanish (IE-Romance) Wikipedia
wiki-da.pt	fine-tune	Danish (IE-Germanic) Wikipedia
wiki-eu.pt	fine-tune	Basque (Basque) Wikipedia
wiki-ja.pt	fine-tune	Japanese (Japanese) Wikipedia
wiki-ro.pt	fine-tune	Romanian (IE-Romance) Wikipedia
wiki-fi.pt	fine-tune	Finnish (Uralic) Wikipedia
wiki-id.pt	fine-tune	Indonesian (Austronesian) Wikipedia
wiki-kk.pt	fine-tune	Kazakh (Turkic) Wikipedia
wiki-he.pt	fine-tune	Hebrew (Afro-Asiatic) Wikipedia
wiki-ur.pt	fine-tune	Urdu (IE-Indic) Wikipedia
wiki-fa.pt	fine-tune	Persian (IE-Iranian) Wikipedia

Experiments

Emergent Communication (EC) Game

This part aims to generate emergent langauge corpus for downstream tasks. Download image_features from Google Drive to ./ec-pretrain/data. To run the emergent communication training,

cd ec-game
python train.py

Some major options:

• --dataset: use Conceptual Captions (cc) or MS-COCO (coco_2014) dataset.
• --vocab_size: Vocabulary size (default 4035).
• --seq_len: Sequence length limit (default 15).

Such a game training automatically stores EC agents (e.g. ./ckpt/cc_vocab_4035_seq_15_reset_-1_nlayers_1/run77926/model_90.6_1000_4035.pt) and emergent language corpora (e.g. ./ckpt/cc_vocab_4035_seq_15_reset_-1_nlayers_1/run77926/model_90.6_1000_4035.pt-cc.pt, which can be used in place of lm_corpora/cc.pt from Google Drive) from different training steps. In the example, 90.6_1000_4035 represents game accuracy, game training steps, and game vocabulary size respectively.

Language Modeling Transfer

This part aims to reproduce Figure 2 of the paper. Download lm_corpora from Google Drive to ./ec-pretrain/data.

To run the pre-training,

export size=2 # 2,5,10,15,30
export pt_name="wiki-es" # "paren-zipf", "cc"
. pretrain.sh

To run the fine-tuning,

export size=2 # 2,5,10,15,30
export pt_name="wiki-es" # "paren-zipf", "cc"
export ft_name="wiki-ro"
export ckpt=3000
. finetune.sh

Meaning of variables above:

• size: Token size (million) of pre-training corpus ([2, 5, 10, 15, 30]).
• pt_name: Name of pre-training corpus (["wiki-es", "paren-zipf", "cc"]).
• ft_name: Name of fine-tuning corpus (["wiki-ro", "wiki-da.pt]).
• ckpt: Which pre-training checkpoint to use for fine-tuning (default 3000).

Acknowledgements

The EC part of the code is based on ECNMT, which was partly based on Translagent.

The LM part of the code is based on Huggingface run_clm.py.

The datasets for our EC experiments include MS COCO and Conceptual Captions.

The datasets for our LM experiments derive from tilt-transfer.

Please cite these resources accordingly. For any question, contact Shunyu.

ec-nl's Issues

Add license to project

Would it be possible license this code under a free software license (https://opensource.org/licenses)? Currently, there is no license displayed in this project, so it is not clear if incorporating this code into other projects is allowed. Thanks.

Dataset coverage during training

Hello @ysymyth ,
If I have understood correctly, during each epoch out of all the N (=total images in the train dataset) only a set of n (=batch size*number of distracting images) is used as listener images here. And n' (=batch size) is used as speaker images here. Hence it is a possibility that some images might be used repetitively and some may even not be used. How can we make sure that each of the image in training data is used as a speaker image at least once?

Multi-GPU support

How can I train the model (EC game - used for generating the emergent language) on the multiple GPUs?

What are the necessary changes that need to be made to the code?

I really appreciate any help you can provide.

Error loading coco files.

Hi,

I am trying to replicate your codebase. The Readme instruction says we should put the files downloaded from the image_features google drive folder into ./ec-pretrain/data. I created a directory called ec-pretrain/data and added the files there but whenever I run train.py for the for the Emergent Communication Game, I receive this error below:

_Entering Main
Traceback (most recent call last):
File "train.py", line 73, in
data = torch.load(feat_path)
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 579, in load
with _open_file_like(f, 'rb') as opened_file:
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 230, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/home/mogunleye/miniconda3/envs/ec-nl1/lib/python3.6/site-packages/torch/serialization.py", line 211, in init
super(open_file, self).init(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: './data/coco.pt'

Then I thought maybe I should put the data in ./data/ instead of ./ec-pretrain/data but I still get the same error.

I was wondering if I have done something wrong?

Thanks and looking forward to hearing from you.