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As conversational agents become integral parts of many aspects of our lives, current approaches are reaching bottlenecks of performance that require increasing amounts of data or increasingly powerful models. It is also becoming clear that such agents are here to stay and accompany us for long periods of time. If we are, therefore, to design agents that can deeply understand our world and evolve with it, we need to take a step back and revisit the trade-offs we have made in the current state of the art models. This paper argues that a) we need to shift from slot filling into a more realistic conversation paradigm; and b) that, to realize that paradigm, we need models that are able to handle concrete and abstract entities as well as evolving relations between them.
To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we exp
Building a socially intelligent agent involves many challenges, one of which is to track the agents mental state transition and teach the agent to make rational decisions guided by its utility like a human. Towards this end, we propose to incorporate
Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do no
We argue that a key challenge in enabling usable and useful interactive task learning for intelligent agents is to facilitate effective Human-AI collaboration. We reflect on our past 5 years of efforts on designing, developing and studying the SUGILI