Classification of discourse functions of affirmative words in spoken dialogue

AGUSTÍN GRAVANO; STEFAN BENUS; JULIA HIRSCHBERG; SHIRA MITCHELL; ILIA VOVSHA

Amberes

Congreso; 8th International Conference on Spoken Language Processing (Interspeech 2007); 2007

International Speech Communication Association (ISCA)

We present results of a series of machine learning experiments that address the classification of the discourse function of single affirmative cue words such as ´alright´, ´okay´ and ´mm-hm´ in a spoken dialogue corpus. We suggest that a simple discourse/sentential distinction is not sufficient for such words and propose two additional classification sub-tasks: identifying (a) whether such words convey acknowledgment or agreement, and (b) whether they cue the beginning or end of a discourse segment. We also study the classification of each individual word into its most common discourse functions. We show that models based on contextual features extracted from the time-aligned transcripts approach the error rate of trained human aligners.