Source code for NAACL 2019 paper: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
- Overview of DiPS during decoding to generate k paraphrases. At each time step, a set of N sequences V(t) is used to determine k < N sequences (X∗) via submodular maximization . The above figure illustrates the motivation behind each submodular component. Please see Section 4 in the paper for details.
- Please use/check
diverse_paraphrase
in NL-Augmenter for the transformer-model version. Diverse-Paraphrase: NL-Augmenter.
- compatible with python 3.6
- dependencies can be installed using
requirements.txt
Download the following datasets:
Extract and place them in the data
directory. Path : data/<dataset-folder-name>
.
A sample dataset folder might look like data/quora/<train/test/val>/<src.txt/tgt.txt>
.
- Download GoogleNews-vectors-negative300.bin.gz into the
data
directory. In case the above link doesn't work, find the zip file here
To get the project's source code, clone the github repository:
$ git clone https://github.com/malllabiisc/DiPS
Install VirtualEnv using the following (optional):
$ [sudo] pip install virtualenv
Create and activate your virtual environment (optional):
$ virtualenv -p python3 venv
$ source venv/bin/activate
Install all the required packages:
$ pip install -r requirements.txt
Install the submodopt package by running the following command from the root directory of the repository:
$ cd ./packages/submodopt
$ python setup.py install
$ cd ../../
python -m src.main -mode train -gpu 0 -use_attn -bidirectional -dataset quora -run_name <run_name>
To use trained embeddings -
python -m src.create_dict -model trained -run_name <run_name> -gpu 0
To use pretrained word2vec
embeddings -
python -m src.create_dict -model pretrained -run_name <run_name> -gpu 0
This will generate the word2vec.pickle
file in data/embeddings
python -m src.main -mode decode -selec submod -run_name <run_name> -beam_width 10 -gpu 0
Please cite the following paper if you find this work relevant to your application
@inproceedings{dips2019,
title = "Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation",
author = "Kumar, Ashutosh and
Bhattamishra, Satwik and
Bhandari, Manik and
Talukdar, Partha",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1363",
pages = "3609--3619"
}
For any clarification, comments, or suggestions please create an issue or contact ashutosh@iisc.ac.in or Satwik Bhattamishra