README.md

Implementation for paper:

Multi-granularity Optimization for Non-autoregressive Translation (To appear in EMNLP 2022)

Paper Link

Setup

pip install -e . 
pip install tensorflow tensorboard sacremoses nltk Ninja omegaconf
pip install 'fuzzywuzzy[speedup]'
pip install hydra-core==1.0.6
pip install sacrebleu==1.5.1
pip install git+https://github.com/dugu9sword/lunanlp.git

Experimental Details

Hyperparameters

Pretrain stage

	EN<->RO	EN<->DE
--validate-interval-updates	300	300
number of tokens per batch	32K	128K
--dropout	0.3	0.1
--max-update	300k	300k

MgMO stage

	EN<->RO	EN<->DE
--validate-interval-updates	300	300
number of tokens per batch	256	1024
--dropout	0.1	0.1
--lr-scheduler	fixed	fixed
--lr	2e-6	2e-6

Arguments

Argument	Description
--n-sample	Number of samples in the search space
--reward-alpha	Coefficient for balancing the sentence probability
--max-length-bias	Max deviation of the predicted length to the golden length during training
--null-input	Set for N&P mode (default)
--rm-scale	The gamma for controling the granularity size
--len-loss	Set to enable length loss during training

Training

We provide a script (run.sh) for replicating the results on WMT'16 EN->RO task. For other directions, you need to adjust the data path and corresponding hyper-paramters where necessary.

Evaluation

We select the best checkpoint for evaluation based on the validation BLEU scores. We set the length beam as 5 for inference. See `run.sh' for details.

Main Files

The implementation is based on Fairseq. We mainly add the following files.

fairseq
├── criterions
│   └── multi_granularity_optimizer.py  # mutli-granularity loss
└── models
    └── nat
        └── cmlm_transformer.py         # implementation for sampling and granularity generation