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
- Cite (ACL):
- Pierre Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos, and Payel Das. 2020. DualTKB: A Dual Learning Bridge between Text and Knowledge Base. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8605–8616, Online. Association for Computational Linguistics.
- Cite (Informal):
- DualTKB: A Dual Learning Bridge between Text and Knowledge Base (Dognin et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.694.pdf
- Data
- ATOMIC, ConceptNet