DualTKB: A Dual Learning Bridge between Text and Knowledge Base

Pierre Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos, Payel Das


Abstract
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.
Anthology ID:
2020.emnlp-main.694
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8605–8616
Language:
URL:
https://aclanthology.org/2020.emnlp-main.694
DOI:
10.18653/v1/2020.emnlp-main.694
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.694.pdf
Video:
 https://slideslive.com/38939126