Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System

Walker, Marilyn A.; Langkilde-Geary, Irene; Hastie, Helen Wright; Wright, Jeremy H.; Gorin, Allen L.

doi:10.1613/jair.971

Cited by 54 publications

(31 citation statements)

References 21 publications

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“…The WOz setup used in this experiment, which replaced ASR and NLU components with a human wizard, also eliminated the majority of error recovery and clarification dialogues which characterise end-to-end systems (Walker et al 2002;McTear et al 2005;Litman et al 2006). Therefore, we need to ask whether the interactions collected in this corpus are realistic.…”

Section: Discussionmentioning

confidence: 99%

The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems

Georgila

Wolters

Moore

et al. 2010

Lang Resources & Evaluation

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We present the MATCH corpus, a unique data set of 447 dialogues in which 26 older and 24 younger adults interact with nine different spoken dialogue systems. The systems varied in the number of options presented and the confirmation strategy used. The corpus also contains information about the users' cognitive abilities and detailed usability assessments of each dialogue system. The corpus, which was collected using a Wizard-of-Oz methodology, has been fully transcribed and annotated with dialogue acts and ''Information State Update'' (ISU) representations of dialogue context. Dialogue act and ISU annotations were This research was supported by the MATCH project (SHEFC-HR04016, performed semi-automatically. In addition to describing the corpus collection and annotation, we present a quantitative analysis of the interaction behaviour of older and younger users and discuss further applications of the corpus. We expect that the corpus will provide a key resource for modelling older people's interaction with spoken dialogue systems.

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Section: Discussionmentioning

confidence: 99%

The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems

Georgila

Wolters

Moore

et al. 2010

Lang Resources & Evaluation

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“…Hastie et al(2002) predicted problematic dialogues from a series of DARPA Communicator dialogues according to user satisfaction rates, task completion predictors and some interaction based features. Walker et al (2002) presented their prediction model on the basis of information the system collected early in the dialogue and in real time. Oulasvirta et al (2006) reported relations between users' satisfaction rates among the goal-level, concept-level, task-level and command-level, and captured a number of qualified user features.…”

Section: Dialog-level Vs Utterance-levelmentioning

confidence: 99%

Problematic Situation Analysis and Automatic Recognition for Chinese Online Conversational System

Xiang¹,

Zhang²,

Zhou³

et al. 2014

Proceedings of the Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Automatic problematic situation recognition (PSR) is important for an online conversational system to constantly improve its performance. A PSR module is responsible of automatically identifying users' un-satisfactions and then sending feedbacks to conversation managers. In this paper, we collect dialogues from a Chinese online chatbot, annotate the problematic situations and propose a framework to predict utterance-level problematic situations by integrating intent and sentiment factors. Different from previous work, the research field is set as open-domain in which very few domain specific textual features could be used and the method is easy to be adapted to other domains. Experimental results show that integrating both intent and sentiment factors gains the best performance.

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“…In [29], like in many other investigations, a subset of AT&T's How-May-I-help-You (HMIHY) database [11] is used. This study differs from other work in that it does not only address the classification of a single utterance, but the annotation of entire dialogs.…”

Section: Classification Of Emotionmentioning

confidence: 99%

Getting closer: tailored human–computer speech dialog

Metze

Englert

Bub

et al. 2008

Univ Access Inf Soc

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This paper presents an advanced call center, which adapts presentation and interaction strategy to properties of the caller such as age, gender, and emotional state. User studies on interactive voice response (IVR) systems have shown that these properties can be used effectively to ''tailor'' services to users or user groups who do not maintain personal preferences, e.g., because they do not use the service on a regular basis. The adopted approach to achieve individualization of services, without being able to personalize them, is based on the analysis of a caller's voice. This paper shows how this approach benefits service providers by being able to target entertainment and recommendation options. It also shows how this analysis at the same time benefits the customer, as it can increase accessibility of IVR systems to user segments which have particular expectations or which do not cope well with a ''one size fits all'' system. The paper summarizes the authors' current work on component technologies, such as emotion detection, age and gender recognition on telephony speech, and presents results of usability and acceptability tests as well as an architecture to integrate these technologies in future multi-modal contact centers. It is envisioned that these will eventually serve customers with an avatar representation of an agent and tailored interaction strategies, matching powerful output capabilities with advanced analysis of the user's input.

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Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System

Cited by 54 publications

References 21 publications

The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems

The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems

Problematic Situation Analysis and Automatic Recognition for Chinese Online Conversational System

Getting closer: tailored human–computer speech dialog

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