It allows the learning agents to consider different types of decisions interdependently that affect the trade-off between detail and efficiency in situated ...
This is particularly true in interactive and situated settings. In this paper we present a novel approach for situated Natural Language Generation in dialogue ...
Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions ...
The model is trained from human–human corpus data and learns particularly to balance the trade-off between efficiency and detail in giving instructions: the ...
Situated NLG can be defined as generation in an enriched physical context, including features of a (real or virtual) environment, such as landmarks and users.
Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue. In Proceedings of the 13th ...
Hierarchical joint learning for natural language generation
www.semanticscholar.org › paper › Hier...
A joint optimisation framework for situated NLG that is based on Hierarchical Reinforcement Learning combined with graphical models and will learn the best ...
This is particularly true in interactive and situated settings. In this paper we present a novel approach forsituated Natural Language Generationin dialogue ...
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Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive out- puts, models have difficulty tracking long-term ...
Missing: situated | Show results with:situated
Sep 20, 2023 · Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions. However, ...
Missing: situated | Show results with:situated