DeepSentiPeer

Harnessing sentiment in the review texts to recommend peer-review decisions

Ghosal, Tirthankar, et al. "DeepSentiPeer: Harnessing sentiment in review texts to recommend peer review decisions." Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

This repository contains the updated code to the above paper. The code now supports SciBERT sentence encoder.

The organization of is as follows:

.
+--`prepare_data.py`: generate and write the review and paper embeddings in .json file.
+--`utils.py`: Relevant utility code for reading and embedding data
+--`run_model.py`: Train the model (currently, supports only the Recommendation Task)

To get started, follow:

$python prepare_data.py ./2018
$python run_model.py --mode RECOMMENDATION

Note: prepare_data.py may run for many hours depending on the size of the data. Also set the trainig data and validation data path properly.

The run_model.py has the following settings:

usage: run_model.py [-h] [--batch_size BATCH_SIZE] [--dropout DROPOUT]
                    [--l2 L2] [--learning_rate LEARNING_RATE] [--mode MODE]
                    [--datadir DATADIR] [--ckpdir CKPDIR]
                    [--exp_name EXP_NAME]

optional arguments:
  -h, --help            show this help message and exit
  --batch_size BATCH_SIZE
                        batch size to train the model (default: 32)
  --dropout DROPOUT     dropout probability (default: 0.5)
  --l2 L2               l2 weight decay penalty (default: 0.007)
  --learning_rate LEARNING_RATE
                        learning rate for the gradient based Algorithm
                        (default: 0.001)
  --mode MODE           Task mode, choose from [RECOMMENDATION, DECISION]
                        (default: RECOMMENDATION)
  --datadir DATADIR     Path to the Dataset (default: ./2018)
  --ckpdir CKPDIR       Path to save the trained models (default: ./MODELS)
  --exp_name EXP_NAME   Name of the experiment, model and params will be saved
                        with this name (default: default)