Skip to content

Latest commit

 

History

History
91 lines (54 loc) · 2.93 KB

README.md

File metadata and controls

91 lines (54 loc) · 2.93 KB
Raw

LaSS: Learning Language Specific Sub-network for Multilingual Machine Translation

This is the repo for ACL2021 paper Learning Language Specific Sub-network for Multilingual Machine Translation.

paper

Introduction

LaSS, representing Language Specific Sub-network, is a single unified multilingual MT model. LaSS aims at alleviating the well-known parameter interference issue in multilingual MT by accommodating one sub-network for each language pair. Extensive experiments demonstrate the efficacy of LaSS and its strong generalization performance in different scenarios.

Pre-requisite

pip3 install -r requirements.txt

Pipeline

The pipeline contains 4 steps:

  1. Train a vanilla multilingual baseline
  2. Fine-tune the baseline for each language pair
  3. Obtain the masks from the fine-tuned model
  4. Continue training the vanilla multilingual baseline with the obtained masks

Data Processing

Before the training phase, you need to prepare the data. In general, data processing contains the following steps:

  • Data filtering
  • Data deduplication
  • Learning/Applying joint BPE vocabulary
  • Data Cleaning

For IWSLT we used in the paper, we directly use this scripts.

For WMT, we collect data from the official WMT website. For details please refer to the appendix of our paper.

We provide some data preprocessing scripts for reference.

Multilingual baseline

We first train a vanilla multilingual baseline.

bash scripts/train.sh —config baseline.yml

Fine-tune the baseline

After obtaining the vanilla multilingual baseline, we need to fine-tune the baseline for each language pair.

bash scripts/train.sh —config finetune.yml

After fine-tuning, we obtain n models, where n represents the number of language pairs we use.

Obtain the masks

For each language pair, we need to prune the α percent lowest weights to obtain the sub-networks.

python3 toolbox/generate_mask.py —checkpoint-path xx —mask-path /path/to/destination —gen-mask-with-prob —mask-prob α —gen-part all —exclude-output-proj

Training with masks

The last step is to continue training the vanilla multilingual model with the obtained masks.

bash scripts/train.sh —config multilingual.yml

The yaml config mentioned above can be found in here.

Evaluation

You can evaluate the trained model with the following script:

bash scripts/evaluate.sh --config config.yml --checkpoint-name xxx --lang-pairs x-y --evaluate-bin /path/to/your/data
  • --config is the training config.
  • --lang-pairs is not necessary. If not available, the script will evaluate all the language pair in the config.
  • --evaluate-bin is also not necessary. If not available, the script will load the data from data_bin in the config.