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spoken dialogue systems ( SDSs ) is
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. The automatic speech recognition
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current and future research on
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. In this paper we describe the
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shallow processing and cooperative
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, the pet ( processing errors
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provides the larger context for the
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architecture . RavenClaw is a
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for Computational Linguistics
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in the RavenClaw Dialog Management
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for Computational Linguistics
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in the RavenClaw Dialog Management
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which automatically tune their
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behaviors to the characteristics
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are violated . At that ~ nt ,
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procedures based on meta-rules
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adaptive and scalable approach for
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in task-oriented spoken dialog
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unfamiliar nouns are handled by the
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routines ( Section 5.0 ) . While
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test-bed for evaluating the proposed
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architec - ture . More generally
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. 2 . APPROACH Our approach to
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in information extraction involves
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provide the mechanisms for robust
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at the dialog management level
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conversational skills , such as
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( discussed extensively in Section
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lead to the best results . The
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architecture we describe in this
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necessary support for conceptlevel
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. Dialog Stack Dialog Engine
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creation time , including all
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. The mean time to repair errors
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milliseconds after each word .
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The major difficulty facing users
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Framework Abstract We describe the
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architectture underlying the
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within and across tasks . 3 The
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Architecture The error handling
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