Research Area
Conversational AI
Conversational systems are becoming an integral part of our daily routines. Every day, millions of people use natural-language interfaces via in-home devices, phones, or messaging channels such as Messenger. We strive to create new conversational technologies that have a deep understanding of the conversation and the context around it and deliver a personalized experience to the user that is both task oriented and empathic.
The next generation of conversational AI systems will be multi-modal and pro-active, integrating cues across several modalities to provide creative and on-spot response to the users.
Latest Publications
Engaging Image Chat: Modeling Personality in Grounded Dialogue
To achieve the long-term goal of machines being able to engage humans in conversation, our models should be engaging.
What makes a good conversation? How controllable attributes affect human judgments
In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
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