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Spoken dialogue data

Xem 1-13 trên 13 kết quả Spoken dialogue data
  • Natural Language Processing applications often require large amounts of annotated training data, which are expensive to obtain. In this paper we investigate the applicability of Co-training to train classifiers that predict emotions in spoken dialogues. In order to do so, we have first applied the wrapper approach with Forward Selection and Naïve Bayes, to reduce the dimensionality of our feature set. Our results show that Co-training can be highly effective when a good set of features are chosen. ...

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  • We present a novel approach to Information Presentation (IP) in Spoken Dialogue Systems (SDS) using a data-driven statistical optimisation framework for content planning and attribute selection. First we collect data in a Wizard-of-Oz (WoZ) experiment and use it to build a supervised model of human behaviour. This forms a baseline for measuring the performance of optimised policies, developed from this data using Reinforcement Learning (RL) methods.

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  • We present and evaluate a new model for Natural Language Generation (NLG) in Spoken Dialogue Systems, based on statistical planning, given noisy feedback from the current generation context (e.g. a user and a surface realiser). We study its use in a standard NLG problem: how to present information (in this case a set of search results) to users, given the complex tradeoffs between utterance length, amount of information conveyed, and cognitive load. We set these trade-offs by analysing existing MATCH data.

    pdf9p bunthai_1 06-05-2013 29 2   Download

  • We present a data-driven approach to learn user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to understand in technical domains where users may not know the technical ‘jargon’ names of the domain entities. In such cases, dialogue systems must be able to model the user’s (lexical) domain knowledge and use appropriate referring expressions.

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  • We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on user’s knowledge or typical kinds of users, the user model we propose is more comprehensive. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. We obtained reasonable classification accuracy for all dimensions.

    pdf8p bunbo_1 17-04-2013 34 1   Download

  • The development of multi-channel digital broadcasting has generated a demand not only for new services but also for smart and highly functional capabilities in all broadcast-related devices. This is especially true of the television receivers on the viewer's side. With the aim of achieving a friendly interface that anybody can use with ease, we built a prototype interface system that operates a television through voice interactions using natural language.

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  • Spoken dialogue systems would be more acceptable if they were able to produce backchannel continuers such as mm-hmm in naturalistic locations during the user's utterances. Using the HCRC Map Task Corpus as our data source, we describe models for predicting these locations using only limited processing and features of the user's speech that are commonly available, and which therefore could be used as a lowcost improvement for current systems. The baseline model inserts continuers after a predetermined number of words. ...

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  • We use a machine learner trained on a combination of acoustic and contextual features to predict the accuracy of incoming n-best automatic speech recognition (ASR) hypotheses to a spoken dialogue system (SDS). Our novel approach is to use a simple statistical User Simulation (US) for this task, which measures the likelihood that the user would say each hypothesis in the current context. Such US models are now common in machine learning approaches to SDS, are trained on real dialogue data, and are related to theories of “alignment” in psycholinguistics.

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  • An increasing number of telephone services are offered in a fully automatic way with the help of speech technology. The underlying systems, called spoken dialogue systems (SDSs), possess speech recognition, speech understanding, dialogue management, and speech generation capabilities, and enable a more-or-less natural spoken interaction with the human user. Nevertheless, the principles underlying this type of interaction are different from the ones which govern telephone conversations between humans, because of the limitations of the machine interaction partner.

    pdf385p tailieuvip13 24-07-2012 38 5   Download

  • This paper describes the application of the PARADISE evaluation framework to the corpus of 662 human-computer dialogues collected in the June 2000 Darpa Communicator data collection. We describe results based on the standard logfile metrics as well as results based on additional qualitative metrics derived using the DATE dialogue act tagging scheme. We show that performance models derived via using the standard metrics can account for 37% of the variance in user satisfaction, and that the addition of DATE metrics improved the models by an absolute 5%. ...

    pdf8p bunrieu_1 18-04-2013 27 2   Download

  • Spoken dialogue systems promise efficient and natural access to information services from any phone. Recently, spoken dialogue systems for widely used applications such as email, travel information, and customer care have moved from research labs into commercial use. These applications can receive millions of calls a month. This huge amount of spoken dialogue data has led to a need for fully automatic methods for selecting a subset of caller dialogues that are most likely to be useful for further system improvement, to be stored, transcribed and further analyzed. ...

    pdf8p bunmoc_1 20-04-2013 28 1   Download

  • We describe a domain-independent semantic interpretation architecture suitable for spoken dialogue systems, which uses a decision-list method to effect a transparent combination of rule-based and data-driven approaches. The architecture has been implemented and evaluated in the context of a mediumvocabulary command and control task.

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  • We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both ruleinduction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline.

    pdf8p bunrieu_1 18-04-2013 50 3   Download

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