ml-lab / reinforce-paraphrase-generation

This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019).

A Unified Reinforcement Learning Framework for Pointer Generator Model

This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation".

Useage

Training

1. Model Setting: modify the path where the model will be saved.

vim config.py
log_root = os.path.join(root_dir, "Reinforce-Paraphrase-Generation/log_twitter")

2. Pre-train: train the standard pointer-generator model with supervised learning from scratch.

python train.py

3. Fine-tune: modify the training mode and the path where the fine-tuned model will be saved.

vim config.py
log_root = os.path.join(root_dir, "Reinforce-Paraphrase-Generation/log_rl")
mode = "RL"

Fine tune the pointer-generator model with REINFORCE algorithm.

python train.py -m ../log_twitter/best_model/model_best_XXXXX

Decoding & Evaluation

1. Decoding: first, specify the model path.

vim config.py
log_root = os.path.join(root_dir, "Reinforce-Paraphrase-Generation/log_twitter")

Second, apply beam search to generate sentences on test set:

python decode.py ../log_twitter/best_model/model_best_XXXXX

2. Evaluation:

   • The average BLEU score will show up automatically in the terminal after finishing decoding.

   • If you want to get the ROUGE scores, you should first intall pyrouge, here is the guidance. Then, you can uncomment the code snippet specified in utils.py and decode.py. Finally, run decode.py to get the ROUGE scores.