In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality transfers. We examine different model architectures, and evaluation metrics, proposing a novel Commonsense KB completion metric tailored for generative models. Extensive experimental results show that the proposed method compares very favorably to the existing baselines. This approach is a viable step towards a more advanced system for automatic KB construction/expansion and the reverse operation of KB conversion to coherent textual descriptions.
@article{arxiv.2010.14660,
title = {DualTKB: A Dual Learning Bridge between Text and Knowledge Base},
author = {Pierre L. Dognin and Igor Melnyk and Inkit Padhi and Cicero Nogueira dos Santos and Payel Das},
journal= {arXiv preprint arXiv:2010.14660},
year = {2020}
}
Comments
Equal Contributions of Authors Pierre L. Dognin, Igor Melnyk, and Inkit Padhi. Accepted at EMNLP'20