Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain

Mingming Sun, Xu Li, Ping Li


Abstract
We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent — Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.
Anthology ID:
D18-1236
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2119–2130
Language:
URL:
https://aclanthology.org/D18-1236
DOI:
10.18653/v1/D18-1236
Bibkey:
Cite (ACL):
Mingming Sun, Xu Li, and Ping Li. 2018. Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2119–2130, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (Sun et al., EMNLP 2018)
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PDF:
https://aclanthology.org/D18-1236.pdf