Automatic acquisition of probabilistic dialogue models

Author

Kita, Kenji ; Fukui, Yoshikazu ; Nagata, Masaaki ; Morimoto, Tsuyoshi

Author_Institution

Fac. of Eng., Tokushima Univ., Japan

Volume

1

fYear

1996

fDate

3-6 Oct 1996

Firstpage

196

Abstract

In the work described, we automatically deduce dialogue structures from a corpus with probabilistic methods. Each utterance in the corpus is annotated with a speaker label and an utterance type called IFT (Illocutionary Force Type). We use an ergodic HMM (hidden Markov model) and the ALERGIA algorithm, an algorithm for learning probabilistic automata by means of state merging, to model the speaker-IFT sequences. Our experiments successfully extract typical dialogue structures such as turn taking and speech act sequencing

Keywords

hidden Markov models; interactive systems; natural language interfaces; probabilistic automata; speech recognition; ALERGIA algorithm; IFT; Illocutionary Force Type; automatic acquisition; corpus; dialogue structure deduction; dialogue structures; ergodic HMM; hidden Markov model; probabilistic automata; probabilistic dialogue models; probabilistic methods; speaker label; speaker-IFT sequences; speech act sequencing; state merging; turn taking; utterance type; Communication systems; Databases; Hidden Markov models; Laboratories; Large-scale systems; Learning automata; Merging; Natural language processing; Natural languages; Speech;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607075

Filename

607075