Implementation of the neural natural logic paper on natural language inference.
Our model combines Natural Logic from Stanford and the neural network. We squeeze intrepretability from the black box neural model by forcing it to learn and reason according to the natural logic framework. This research is in its early stage and we are still working to enhance it. We cleaned our experiment code and release the core in this repo. Please contact the first author feng.yufei@queensu.ca for more info
./preprocess/med_2hop.tsv
https://drive.google.com/file/d/17xyD31Aq8XsVLBVKKn4RagJRmeDNlsQ_/view?usp=sharing or https://queensuca-my.sharepoint.com/personal/17yf48_queensu_ca/Documents/Attachments/checkpoints.zip or send me (feng.yufei@queensu.ca) an email
https://drive.google.com/file/d/1909m38xsSsyaiQMXwcQMGaacg3qKhA_d/view?usp=sharing
@inproceedings{feng2020exploring,
title={Exploring End-to-End Differentiable Natural Logic Modeling},
author={Feng, Yufei, Ziou Zheng, and Liu, Quan and Greenspan, Michael and Zhu, Xiaodan},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={1172--1185},
year={2020}
}