Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

He He, Anusha Balakrishnan, Mihail Eric, Percy Liang


Abstract
We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.
Anthology ID:
P17-1162
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1766–1776
Language:
URL:
https://www.aclweb.org/anthology/P17-1162
DOI:
10.18653/v1/P17-1162
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1162.pdf
Note:
 P17-1162.Notes.pdf