In this paper , we investigate the problem of automatically predicting
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segment boundaries
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in
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spoken multiparty dialogue
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.
#11410In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue.
other,67-5-E06-1035,ak
Examination of the effect of
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features
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shows that predicting top-level and predicting
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subtopic boundaries
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are two distinct tasks : ( 1 ) for predicting
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subtopic boundaries
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, the
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lexical cohesion-based approach
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alone can achieve competitive results , ( 2 ) for predicting
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top-level boundaries
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, the
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machine learning approach
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that combines
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lexical-cohesion and conversational features
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performs best , and ( 3 )
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conversational cues
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, such as
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cue phrases
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and
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overlapping speech
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, are better indicators for the
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top-level prediction task
</term>
.
#11530Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.
other,5-6-E06-1035,ak
We also find that the
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transcription errors
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inevitable in
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ASR output
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have a negative impact on
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models
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that combine
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lexical-cohesion and conversational features
</term>
, but do not change the general preference of approach for the two tasks .
#11550We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.