17,069 research outputs found
Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems using Reinforcement Learning
Abstract Adaptive generation of referring expressions in dialogues is beneficial in terms of grounding between the dialogue partners. However, handcoding adaptive REG policies is hard. We present a reinforcement learning framework to automatically learn an adaptive referring expression generation policy for spoken dialogue systems
Learning user modelling strategies for adaptive referring expression generation in spoken dialogue systems
We address the problem of dynamic user modelling for referring expression generation in spoken dialogue systems, i.e how a spoken dialogue system should choose
referring expressions to refer to domain entities to users with different levels of domain
expertise, whose domain knowledge is initially unknown to the system. We approach
this problem using a statistical planning framework: Reinforcement Learning techniques in Markov Decision Processes (MDP).
We present a new reinforcement learning framework to learn user modelling strategies for adaptive referring expression generation (REG) in resource scarce domains
(i.e. where no large corpus exists for learning). As a part of the framework, we present
novel user simulation models that are sensitive to the referring expressions used by
the system and are able to simulate users with different levels of domain knowledge.
Such models are shown to simulate real user behaviour more closely than baseline user
simulation models.
In contrast to previous approaches to user adaptive systems, we do not assume that
the user’s domain knowledge is available to the system before the conversation starts.
We show that using a small corpus of non-adaptive dialogues it is possible to learn an
adaptive user modelling policy in resource scarce domains using our framework. We
also show that the learned user modelling strategies performed better in terms of adaptation than hand-coded baselines policies on both simulated and real users. With real
users, the learned policy produced around 20% increase in adaptation in comparison
to the best performing hand-coded adaptive baseline. We also show that adaptation to
user’s domain knowledge results in improving task success (99.47% for learned policy vs 84.7% for hand-coded baseline) and reducing dialogue time of the conversation
(11% relative difference). This is because users found it easier to identify domain
objects when the system used adaptive referring expressions during the conversations
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Reinforced Natural Language Interfaces via Entropy Decomposition
In this paper, we study the technical problem of developing conversational
agents that can quickly adapt to unseen tasks, learn task-specific
communication tactics, and help listeners finish complex, temporally extended
tasks. We find that the uncertainty of language learning can be decomposed to
an entropy term and a mutual information term, corresponding to the structural
and functional aspect of language, respectively. Combined with reinforcement
learning, our method automatically requests human samples for training when
adapting to new tasks and learns communication protocols that are succinct and
helpful for task completion. Human and simulation test results on a referential
game and a 3D navigation game prove the effectiveness of the proposed method
Towards Integration of Cognitive Models in Dialogue Management: Designing the Virtual Negotiation Coach Application
This paper presents an approach to flexible and adaptive dialogue management driven by cognitive modelling of human dialogue behaviour. Artificial intelligent agents, based on the ACT-R cognitive architecture, together with human actors are participating in a (meta)cognitive skills training within a negotiation scenario. The agent employs instance-based learning to decide about its own actions and to reflect on the behaviour of the opponent. We show that task-related actions can be handled by a cognitive agent who is a plausible dialogue partner. Separating task-related and dialogue control actions enables the application of sophisticated models along with a flexible architecture in which various alternative modelling methods can be combined. We evaluated the proposed approach with users assessing the relative contribution of various factors to the overall usability of a dialogue system. Subjective perception of effectiveness, efficiency and satisfaction were correlated with various objective performance metrics, e.g. number of (in)appropriate system responses, recovery strategies, and interaction pace. It was observed that the dialogue system usability is determined most by the quality of agreements reached in terms of estimated Pareto optimality, by the user's negotiation strategies selected, and by the quality of system recognition, interpretation and responses. We compared human-human and human-agent performance with respect to the number and quality of agreements reached, estimated cooperativeness level, and frequency of accepted negative outcomes. Evaluation experiments showed promising, consistently positive results throughout the range of the relevant scales
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