Code for our EACL-2021 paper "Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs".
If you find that the code is useful in your research, please consider citing our paper.
@inproceedings{Huang2021synpg,
author = {Kuan-Hao Huang and Kai-Wei Chang},
title = {Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs},
booktitle = {Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
year = {2021},
}
- Python=3.7.10
$ pip install -r requirements.txt
- Download pretrained SynPG or Pretrained SynPG-Large as well as pretrained parse generator, and put them to
./model - Run
scripts/demo.shor the following command to generatedemo/output.txt - How to generate input.txt giving a set of input data?
java -Xmx12g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -threads 1 -annotators tokenize,ssplit,pos,parse -ssplit.eolonly -filelist files.txt -outputFormat text -parse.model edu/stanford/nlp/models/srparser/englishSR.ser.gz -outputDirectory ./output/
python generate.py \
--synpg_model_path ./model/pretrained_synpg.pt \
--pg_model_path ./model/pretrained_parse_generator.pt \
--input_path ./demo/input.txt \
--output_path ./demo/output.txt \
--bpe_codes_path ./data/bpe.codes \
--bpe_vocab_path ./data/vocab.txt \
--bpe_vocab_thresh 50 \
--dictionary_path ./data/dictionary.pkl \
--max_sent_len 40 \
--max_tmpl_len 100 \
--max_synt_len 160 \
--temp 0.5 \
--seed 0
- Download data and put them under
./data/ - Download glove.840B.300d.txt and put it under
./data/ - Run
scripts/train_synpg.shor the following command to train SynPG
python train_synpg.py \
--model_dir ./model \
--output_dir ./output \
--bpe_codes_path ./data/bpe.codes \
--bpe_vocab_path ./data/vocab.txt \
--bpe_vocab_thresh 50 \
--dictionary_path ./data/dictionary.pkl \
--train_data_path ./data/train_data.h5 \
--valid_data_path ./data/valid_data.h5 \
--emb_path ./data/glove.840B.300d.txt \
--max_sent_len 40 \
--max_synt_len 160 \
--word_dropout 0.4 \
--n_epoch 5 \
--batch_size 64 \
--lr 1e-4 \
--weight_decay 1e-5 \
--log_interval 250 \
--gen_interval 5000 \
--save_interval 10000 \
--temp 0.5 \
--seed 0
- Run
scripts/train_parse_generator.shor the following command to train the parse generator
python train_parse_generator.py \
--model_dir ./model \
--output_dir ./output_pg \
--dictionary_path ./data/dictionary.pkl \
--train_data_path ./data/train_data.h5 \
--valid_data_path ./data/valid_data.h5 \
--max_sent_len 40 \
--max_tmpl_len 100 \
--max_synt_len 160 \
--word_dropout 0.2 \
--n_epoch 5 \
--batch_size 32 \
--lr 1e-4 \
--weight_decay 1e-5 \
--log_interval 250 \
--gen_interval 5000 \
--save_interval 10000 \
--temp 0.5 \
--seed 0
- Download testing data and put them under
./data/ - Run
scripts/eval.shor the following command to evaluate SynPG
python eval_generate.py \
--test_data ./data/test_data_mrpc.h5 \
--dictionary_path ./data/dictionary.pkl \
--model_path ./model/pretrained_synpg.pt \
--output_dir ./eval/ \
--bpe_codes ./data/bpe.codes \
--bpe_vocab ./data/vocab.txt \
--bpe_vocab_thresh 50 \
--max_sent_len 40 \
--max_synt_len 160 \
--word_dropout 0.0 \
--batch_size 64 \
--temp 0.5 \
--seed 0 \
python eval_calculate_bleu.py --ref ./eval/target_sents.txt --input ./eval/outputs.txt
The BLEU scores should be similar to the following.
| MRPC | PAN | Quora | |
|---|---|---|---|
| SynPG | 26.2 | 27.3 | 33.2 |
| SynPG-Large | 36.2 | 27.1 | 34.7 |
One main advantage of SynPG is that SynPG learns the paraphrase generation model without using any paraphrase pairs. Therefore, it is possible to fine-tune SynPG with the texts (without using the ground truth paraphrases) in the target domain when those texts are available. This fine-tuning step would significantly improve the quality of paraphrase generation in the target domain, as shown in our paper.
- Download testing data and put them under
./data/ - Run
scripts/finetune_synpg.shor the following command to finetune SynPG
python finetune_synpg.py \
--model_dir ./model_finetune \
--model_path ./model/pretrained_synpg.pt \
--output_dir ./output_finetune \
--bpe_codes_path ./data/bpe.codes \
--bpe_vocab_path ./data/vocab.txt \
--bpe_vocab_thresh 50 \
--dictionary_path ./data/dictionary.pkl \
--train_data_path ./data/test_data_mrpc.h5 \
--valid_data_path ./data/test_data_mrpc.h5 \
--max_sent_len 40 \
--max_synt_len 160 \
--word_dropout 0.4 \
--n_epoch 50 \
--batch_size 64 \
--lr 1e-4 \
--weight_decay 1e-5 \
--log_interval 250 \
--gen_interval 5000 \
--save_interval 10000 \
--temp 0.5 \
--seed 0
We can observe the significant improvement on BLEU scores from the table below.
| MRPC | PAN | Quora | |
|---|---|---|---|
| SynPG | 26.2 | 27.3 | 33.2 |
| SynPG-Large | 36.2 | 27.1 | 34.7 |
| SynPG-Fine-Tune | 48.7 | 37.7 | 49.8 |
Kuan-Hao Huang / @ej0cl6