arXiv (Cornell University) · 2017 · 28 citations · 19 references
ComputingCommunicationComputer ScienceComputational LinguisticsMachine LearningNatural Language ProcessingAnimal CommunicationDialogue SystemsMammalian CommunicationMultimodal CommunicationMultimodal CorporaMultimodal InteractionBandwidthBandwidth-constrained CommunicationConversational AgentsEmergent CommunicationMultimodal ConversationMultimodal Dialogue SystemsNatural Language Inference
Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.
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