Dialogue knowledge acquisition from annotated corpora

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