Community Regularization of Visually-Grounded Dialog

Autor:	Agarwal, Akshat, Gurumurthy, Swaminathan, Sharma, Vasu, Lewis, Mike, Sycara, Katia
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language. We posit that requiring artificial agents to adhere to the rules of human language, while also requiring them to maximize information exchange through dialog is an ill-posed problem. We observe that humans do not stray from a common language because they are social creatures who live in communities, and have to communicate with many people everyday, so it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent community-based dialog framework where each agent interacts with, and learns from, multiple agents, and show that this community-enforced regularization results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics). Comment: 7 pages, ICML/AAMAS Adaptive Learning Agents Workshop 2018 and CVPR Visual Dialog Workshop 2018. Code available at https://github.com/agakshat/visualdialog-pytorch
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1808.04359 Zobrazit plný text záznamu View this record from Arxiv