Modeling user behavior online for disambiguating user input in a spoken dialogue system

Wang, Fangju; Swegles, Kyle

doi:10.1016/j.specom.2012.06.006

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…and the level of performance reached, e.g. (Wang and Swegles 2013;Li et al 2016;Chen et al 2017ba;Chang et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users

Riou¹,

Jabaian²,

Huet³

et al. 2021

Preprint

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Design of dialogue systems has witnessed many advances lately, yet acquiring huge set of data remains an hindrance to their fast development for a new task or language. Besides, training interactive systems with batch data is not satisfactory. On-line learning is pursued in this paper as a convenient way to alleviate these difficulties. After the system modules are initiated, a single process handles data collection, annotation and use in training algorithms. A new challenge is to control the cost of the on-line learning borne by the user. Our work focuses on learning the semantic parsing and dialogue management modules (speech recognition and synthesis offer ready-for-use solutions). In this context we investigate several variants of simultaneous learning which are tested in user trials. In our experiments, with varying merits, they can all achieve good performance with only a few hundreds of training dialogues and overstep a handcrafted system. The analysis of these experiments gives us some insights, discussed in the paper, into the difficulty for the system's trainers to establish a coherent and constant behavioural strategy to enable a fast and good-quality training phase.

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“…and the level of performance reached, e.g. (Wang and Swegles 2013;Li et al 2016;Chen et al 2017ba;Chang et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users

Riou¹,

Jabaian²,

Huet³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The approach described in [ 28 ] uses other features for the prediction of the user intention, a mixture of morpheme, discourse, and domain level characteristics that are integrated by means of a maximum entropy model. In [ 29 ] a technique is proposed that uses information about the activities performed by the users as a solution to disambiguate their inputs. This information is achieved with a reinforcement learning algorithm and then used by the dialogue manager to decide the next system action.…”

Section: Related Workmentioning

confidence: 99%

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

Griol

Callejas

2016

Computational Intelligence and Neuroscience

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Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.

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“…115 Recently, Wang and Swegles propose a technique that employs knowledge about the user's activity to disambiguate their spoken inputs. 108 A Reinforcement Learning algorithm is proposed to acquire the knowledge and apply it for disambiguation. The interpreted user utterance is then transmitted to the dialog manager to select the next system response.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Griol

Iglesias

Ledezma

et al. 2016

Int. J. Neur. Syst.

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This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

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Modeling user behavior online for disambiguating user input in a spoken dialogue system

Cited by 5 publications

References 23 publications

Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users

Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

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