DualTKB: A Dual Learning Bridge between Text and Knowledge Base
Pierre Dognin, Igor V. Melnyk, Inkit Padhi, Cícero Nogueira dos Santos, Payel Das · 2020
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.