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20172026
most citedTraining an adaptive dialogue policy for interactive learning of visually grounded word meanings

7 citations · 27 across the 8 of their papers we have counts for

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cs.CL2026

SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation

Michael Orme, Yanchao Yu, Zhiyuan Tan

Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We present \textbf{SafeCtrl-RL}, an inf…

cs.CL2017★ 7 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.CL2017★ 7 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.CL2017★ 7 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.CL2017★ 3 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…