Emergent Translation in Multi-Agent Communication

PyTorch implementation of the models described in the paper Emergent Translation in Multi-Agent Communication.

We present code for training and decoding both word- and sentence-level models and baselines, as well as preprocessed datasets.

Dependencies

Python

Python 2.7
PyTorch 0.2
Numpy

GPU

CUDA (we recommend using the latest version. The version 8.0 was used in all our experiments.)

Related code

For preprocessing, we used scripts from Moses and Subword-NMT.

Downloading Datasets

The original corpora can be downloaded from (Bergsma500, Multi30k, MS COCO). For the preprocessed corpora see below.

	Dataset
Bergsma500	Data
Multi30k	Data
MS COCO	Data

Before you run the code

Download the datasets and place them in /data/word (Bergsma500) and /data/sentence (Multi30k and MS COCO)
Set correct path in scr_path() from /scr/word/util.py and scr_path(), multi30k_reorg_path() and coco_path() from /src/sentence/util.py

Word-level Models

Running nearest neighbour baselines

$ python word/bergsma_bli.py

Running our models

$ python word/train_word_joint.py --l1 <L1> --l2 <L2>

where <L1> and <L2> are any of {en, de, es, fr, it, nl}

Sentence-level Models

Baseline 1 : Nearest neighbour

$ python sentence/baseline_nn.py --dataset <DATASET> --task <TASK> --src <SRC> --trg <TRG>

Baseline 2 : NMT with neighbouring sentence pairs

$ python sentence/nmt.py --dataset <DATASET> --task <TASK> --src <SRC> --trg <TRG> --nn_baseline

Baseline 3 : Nakayama and Nishida, 2017

$ python sentence/train_naka_encdec.py --dataset <DATASET> --task <TASK> --src <SRC> --trg <TRG> --train_enc_how <ENC_HOW> --train_dec_how <DEC_HOW>

where <ENC_HOW> is either two or three, and <DEC_HOW> is either img, des, or both.

Our models :

$ python sentence/train_seq_joint.py --dataset <DATASET> --task <TASK>

Aligned NMT :

$ python sentence/nmt.py --dataset <DATASET> --task <TASK> --src <SRC> --trg <TRG>

where <DATASET> is multi30k or coco, and <TASK> is either 1 or 2 (only applicable for Multi30k).

Dataset & Related Code Attribution

Moses is licensed under LGPL, and Subword-NMT is licensed under MIT License.
MS COCO and Multi30k are licensed under Creative Commons.

Citation

If you find the resources in this repository useful, please consider citing:

@inproceedings{Lee:18,
  author    = {Jason Lee and Kyunghyun Cho and Jason Weston and Douwe Kiela},
  title     = {Emergent Translation in Multi-Agent Communication},
  year      = {2018},
  booktitle = {Proceedings of the International Conference on Learning Representations},
}

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