@inproceedings{dognin-etal-2020-dualtkb,
title = "{D}ual{TKB}: {A} {D}ual {L}earning {B}ridge between {T}ext and {K}nowledge {B}ase",
author = "Dognin, Pierre and
Melnyk, Igor and
Padhi, Inkit and
Nogueira dos Santos, Cicero and
Das, Payel",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.694",
doi = "10.18653/v1/2020.emnlp-main.694",
pages = "8605--8616",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DualTKB: A Dual Learning Bridge between Text and Knowledge Base
%A Dognin, Pierre
%A Melnyk, Igor
%A Padhi, Inkit
%A Nogueira dos Santos, Cicero
%A Das, Payel
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F dognin-etal-2020-dualtkb
%X 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.
%R 10.18653/v1/2020.emnlp-main.694
%U https://aclanthology.org/2020.emnlp-main.694
%U https://doi.org/10.18653/v1/2020.emnlp-main.694
%P 8605-8616
Markdown (Informal)
[DualTKB: A Dual Learning Bridge between Text and Knowledge Base](https://aclanthology.org/2020.emnlp-main.694) (Dognin et al., EMNLP 2020)
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.