Machine Reasoning: Technology, Dilemma and Future

Nan Duan, Duyu Tang, Ming Zhou


Abstract
Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.
Anthology ID:
2020.emnlp-tutorials.1
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Month:
November
Year:
2020
Address:
Online
Editors:
Aline Villavicencio, Benjamin Van Durme
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2020.emnlp-tutorials.1
DOI:
10.18653/v1/2020.emnlp-tutorials.1
Bibkey:
Cite (ACL):
Nan Duan, Duyu Tang, and Ming Zhou. 2020. Machine Reasoning: Technology, Dilemma and Future. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 1–6, Online. Association for Computational Linguistics.
Cite (Informal):
Machine Reasoning: Technology, Dilemma and Future (Duan et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.emnlp-tutorials.1.pdf