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A corpus-based analysis of route instructions in human-robot interaction
This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was
operated by naïve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and
demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of
this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for
endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well
as the environment
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
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
Do (and say) as I say: Linguistic adaptation in human-computer dialogs
© Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence structure, and acoustic features in dialog, yet little is known about how the alignment mechanism operates in the interaction between users and computer systems let alone how it may be exploited to improve the efficiency of the interaction. This article provides an account of lexical alignment in human–computer dialogs, based on empirical data collected in a simulated human–computer interaction scenario. The results indicate that alignment is present, resulting in the gradual reduction and stabilization of the vocabulary-in-use, and that it is also reciprocal. Further, the results suggest that when system and user errors occur, the development of alignment is temporarily disrupted and users tend to introduce novel words to the dialog. The results also indicate that alignment in human–computer interaction may have a strong strategic component and is used as a resource to compensate for less optimal (visually impoverished) interaction conditions. Moreover, lower alignment is associated with less successful interaction, as measured by user perceptions. The article distills the results of the study into design recommendations for human–computer dialog systems and uses them to outline a model of dialog management that supports and exploits alignment through mechanisms for in-use adaptation of the system’s grammar and lexicon
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Situated grounding and understanding of structured low-resource expert data
Conversational agents are becoming more widespread, varying from social to goaloriented to multi-modal dialogue systems. However, for systems with both visual
and spatial requirements, such as situated robot planning, developing accurate goaloriented dialogue systems can be extremely challenging, especially in dynamic environments, such as underwater or first responders. Furthermore, training data-driven
algorithms in these domains is challenging due to the esoteric nature of the interaction, which requires expert input. We derive solutions for creating a collaborative
multi-modal conversational agent for setting high-level mission goals. We experiment with state-of-the-art deep learning models and techniques and create a new
data-driven method (MAPERT) that is capable of processing language instructions
by grounding the necessary elements using various types of input data (vision from
a map, text and other metadata). The results show that, depending on the task,
the accuracy of data-driven systems can vary dramatically depending on the type
of metadata and the attention mechanisms that are used. Finally, we are dealing
with low-resource expert data and this inspired the use of the Continual Learning
and Human In The Loop methodology with encouraging results
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