Dialogue manager domain adaptation using Gaussian process reinforcement learning

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”

Towards a multimedia knowledge-based agent with social competence and human interaction capabilities

We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.The presented work is funded by the European Commission under the contract number H2020-645012-RIA

Managing adaptive spoken dialogue for Intelligent Environments

Speech interfaces within Intelligent Environments (IEs) must be rendered adaptive to external and internal factors, among those the complexity of the dialogue. Hence, we present HIS-OwlSpeak, a model-driven dialogue manager for Intelligent Environments. It meets the challenges arising from engineering IEs by providing a unified platform comprising adaptivity to a variety of internal and external factors. This work addresses internal adaptivity realized by different modes of dialogue control, i.e., rule-based and probabilistic. For this, the Hidden Information State (HIS) approach-featuring inherent handling of uncertainty in dialogue systems-is applied to a model-driven, solely rule-based dialogue manager. It uses ontologies to specify the dialogue thus separating the specification from the dialogue control. Consequently, all necessary aspects for merging the world of model-driven dialogue management with the HIS approach are presented in detail. Furthermore, the system has been evaluated using two concurrent dialogues of different complexity successfully validating the implementation

Interaction quality estimation in spoken dialogue systems using hybrid-HMMs

Research trends on SDS evaluation are recently focusing on objective assessment methods. Most existing methods, which derive quality for each systemuser-exchange, do not consider temporal dependencies on the quality of previous exchanges. In this work, we investigate an approach for determining Interaction Quality for human-machine dialogue based on methods modeling the sequential characteristics using HMM modeling. Our approach significantly outperforms conventional approaches by up to 4.5% relative improvement based on Unweighted Average Recall metrics

Improving interaction quality recognition using error correction

Determining the quality of an ongoing interaction in the field of Spoken Dialogue Systems is a hard task. While existing methods employing automatic estimation already achieve reasonable results, still there is a lot of room for improvement. Hence, we aim at tackling the task by estimating the error of the applied statistical classification algorithms in a two-stage approach. Correcting the hypotheses using the estimated model error increases performance by up to 4.1 % relative improvement in Unweighted Average Recall

HIS-owlspeak: A model-driven dialogue manager with multiple control modes

In this paper, we present HIS-OwlSpeak, a model-driven dialogue manager for Intelligent Environments. It aims at providing a unified platform offering different modes of dialogue control, i.e., rule-based and probabilistic. For this, the Hidden Information State (HIS) approach is applied to an already published, solely rule-based version of OwlSpeak, which uses ontologies to specify the dialogue and separates the specification from the dialogue control. Consequently, the changes to OwlSpeak necessary for applying the HIS approach are presented in detail. Furthermore, a system test has been performed for a simple dialogue in the flight booking domain successfully validating the implementation. © 2013 IEEE

Interaction Quality: Assessing the quality of ongoing spoken dialog interaction by experts - And how it relates to user satisfaction

This study presents a novel expert-based approach to assess the quality of ongoing Spoken Dialog System (SDS) interactions. We call this approach "Interaction Quality" (IQ). It is an objective measure which relies on statistical classification with Support Vector Machines (SVMs). We compare objective expert IQ annotations of ongoing SDS interactions with subjective User Satisfaction (US) ratings and show that IQ and US correlate (ρ=.66). Expert annotations obviously mirror the subjective user impression to a great extent while they are, above all, much easier to obtain. The IQ score that quantifies the quality of the interaction is generated using the median score of exchange annotations of several experts. US is tracked in a study with 38 users interacting with an SDS. A large, comprehensive set of domain-independent, automatic interaction parameters is introduced to quantify the interaction at arbitrary dialog exchanges. Furthermore, a manually annotated negative emotion feature is added to the parameter set in order to evaluate the contribution of emotions on the classification of IQ and US. For evaluation we use the CMU Let's Go bus information system. The model yields a correlation of ρ=.80 when classifying IQ scores annotated in field data from the CMU system. Furthermore, the model achieves ρ=.74 for predicting US on lab data, and ρ=.89 for IQ on lab data. The presented approach outperforms related studies in the field. Only a marginal contribution of the emotion feature to the performance can be observed, implying that US is not influenced by visible emotions. We analyze causalities and correlations between the interaction parameters and the target variables US/IQ and identify relevant predictors. With the presented paradigm, critical dialogs can be found; once deployed as an online monitoring technique, this paradigm could render SDSs more user friendly and improve user acceptance

First insight into quality-adaptive dialogue

While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone. To overcome this, the user is put into the focus of dialogue management. Hence, an approach for adapting the course of the dialogue to Interaction Quality, an objective variant of user satisfaction, is presented in this work. In general, rendering the dialogue adaptive to user satisfaction enables the dialogue system to improve the course of the dialogue and to handle problematic situations better. In this contribution, we present a pilot study of quality-adaptive dialogue. By selecting the confirmation strategy based on the current IQ value, the course of the dialogue is adapted in order to improve the overall user experience. In a user experiment comparing three different confirmation strategies in a train booking domain, the adaptive strategy performs successful and is among the two best rated strategies based on the overall user experience

User-Centred Spoken Dialogue Management

Adaptivity of intelligent environments to their surroundings provided by the ATRACO Spoken Dialogue Manager is only one means of adaptation. Recent work in Spoken Dialogue Systems focuses on the integration of user-centred adaptation means to alter the content, flow and structure of the ongoing dialogue. In this chapter, we introduce a general user-centred adaptation cycle, accompanied by two implemented adaptation approaches focusing respectively on short-term and long-term goals in human–computer interaction. After motivating the need for short-term and long-term goals to entail different adaptation mechanisms, we provide exemplary adaptation entities for each case with corresponding experiments and implementations. The short-term goal user satisfaction allows for detecting whether the user is not satisfied with the interaction and for triggering counter measures to improve the interaction. As a long-term goal, maintaining human–computer trust attempts to keep users still willing to use the system even if the interaction was confusing

Towards an argumentative dialogue system

In this work we propose a scheme for an Argumentative Dialogue System that allows a user to discuss a certain topic with a virtual agent using natural language. Starting from an agent vs agent case, we address the optimization of the agent strategy by formulating the problem as a Stochastic Game and show that this formalism generally allows the inclusion of additional strategical moves that are not based on the content of the argument. In a second step we propose our approach for the Natural Language Understanding of arguments by combining recent results of argumentation mining with a keyword-based mapping