most citedTraining an adaptive dialogue policy for interactive learning of visually grounded word meanings

7 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2020

Explainable Representations of the Social State: A Model for Social Human-Robot Interactions

Daniel Hernández García, Yanchao Yu, Weronika Sieińska +4

In this paper, we propose a minimum set of concepts and signals needed to track the social state during Human-Robot Interaction. We look into the problem of complex continuous inte…

cs.CL20177 cited

An Ensemble Model with Ranking for Social Dialogue

Ioannis Papaioannou, Amanda Cercas Curry, Jose L. Part +6

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real c…

cs.CL20177 cited

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

Yanchao Yu, Arash Eshghi, Gregory Mills +1

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to…

cs.CL20177 cited

Training an adaptive dialogue policy for interactive learning of visually grounded word meanings

Yanchao Yu, Arash Eshghi, Oliver Lemon

We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/…

cs.CL20173 cited

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

Yanchao Yu, Arash Eshghi, Oliver Lemon

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within…