Detecting Incongruity in PyTorch

PyTorch implementation of Attentive Hierarchical Dual Encoder model from the following paper:

Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder, AAAI-19, paper

Original Tensorflow implementation can be found here [1]

Requirements

• Python 3.6 or greater
• PyTorch 1.2.0

Installation

pip install -r requirements.txt

Download Dataset

Follow instructions from the original Tensorflow repo [1].

Train

NELA 2017

python main.py --data-dir <PATH_TO_NELA_2017_DATA> \
               --max-headline-len 25 \
               --max-para-len 200 \
               --max-num-para 50 \
               --headline-rnn-hidden-dim 200 \
               --word-level-rnn-hidden-dim 200 \
               --paragraph-level-rnn-hidden-dim 100 \
               --lr 0.001 \
               --batch-size 64 \
               --evaluate-test-after-train

References

[1] https://github.com/david-yoon/detecting-incongruity

Cite

Please cite our paper, when you use our code | dataset | model

@inproceedings{yoon2019detecting,
title={Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder},
author={Yoon, Seunghyun and Park, Kunwoo and Shin, Joongbo and Lim, Hongjun and Won, Seungpil and Cha, Meeyoung and Jung, Kyomin},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={33},
pages={791--800},
year={2019}
}