First steps towards statistical modeling of dialogue to predict the speech act type of the next utterance

@article{Nagata1994FirstST,
  title={First steps towards statistical modeling of dialogue to predict the speech act type of the next utterance},
  author={Masaaki Nagata and Tsuyoshi Morimoto},
  journal={Speech Communication},
  year={1994},
  volume={15},
  pages={193-203}
}
We propose a statistical dialogue modeling method based on the information theory and the speech act theory. The dialogue model consists of a trigram of utterances classified by their speech act. It can be used to rule out erroneous speech recognition candidates that are syntactically and semantically correct, but contextually incorrect, by examining whether the utterance candidates form a natural local discourse in terms of speech act sequencing. Since it is based on the information theory, we… CONTINUE READING

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Key Quantitative Results

  • The word perplexity of word bigram with speech act type trigram is 7.27, while that of simple word bigram is 11.6, when the word perplexity of the language models is computed using the 100 keyboard dialogues.

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