PaperRobot: Incremental Draft Generation of Scientific Ideas

Arxiv

Accpeted by 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)

Table of Contents

• Overview
• Requirements
• Quickstart
• Citation

Overview

Requirements

Environment:

• Pytorch 1.1
• Python 3.6 CAUTION!! Model might not be saved and loaded properly under Python 3.5

Data:

• Pubmed term, abstract, conclusion, title dataset

Quickstart

New paper writing

Preprocessing:

Download and unzip the data_pubmed_writing.zip. Put data folder under the New paper writing folder.

Training

Put the type of data after the --data_path. For example, if you want to train an abstract model, put data/pubmed_abstract after --data_path. Put the model directory after the --model_dp For more other options, please check the code.

python train.py --data_path data/pubmed_abstract --model_dp abstract_model/

Test

Put the finished model path after the --model The test.py will provide the score for test set.

python test.py --data_path data/pubmed_abstract --model abstract_model/memory/best_dev_model.pth.tar

Predict an instance

Put the finished model path after the --model The input.py will provide the prediction for customized input.

python input.py --data_path data/pubmed_abstract --model abstract_model/memory/best_dev_model.pth.tar

Citation

@InProceedings{wang2019paperrobot,
  author = 	"Wang, Qingyun and Huang, Lifu and Jiang, Zhiying and Knight, Kevin and Ji, Heng and Bansal, Mohit and Luan, Yi",
  title = 	"PaperRobot: Incremental Draft Generation of Scientific Ideas",
  booktitle = 	"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
  year = 	"2019"
}