This repository contains the source code and data used in our paper "MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER" accepted by ACL-IJCNLP 2021.
The data generated using our labeled sequence translation method can be found in the "data" directory.
cd code/translate; python translate.py
- train lstm-lm on linearized sequences
cd code/lstm-lm;
python train.py \
--train_file PATH/TO/train.linearized.txt \
--valid_file PATH/TO/dev.linearized.txt \
--model_file PATH/TO/model.pt \
--emb_dim 300 \
--rnn_size 512 \
--gpuid 0
- generate linearized sequences
cd code/lstm-lm;
python generate.py \
--model_file PATH/TO/model.pt \
--out_file PATH/TO/out.txt \
--num_sentences 10000 \
--temperature 1.0 \
--seed 3435 \
--max_sent_length 32 \
--gpuid 0
- tools/preprocess.py: sequence linearization
- tools/line2cols.py: convert linearized sequence back to two-column format
- code/lstm-lm/requirements.txt
Please cite our paper if you found the resources in this repository useful.
@inproceedings{liu-etal-2021-mulda,
title = "MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER",
author = "Liu Linlin and
Ding, Bosheng and
Bing, Lidong and
Joty, Shafiq and
Si, Luo and
Miao, Chunyan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL'21)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}