Text Implicates Prosodic Ambiguity: A Corpus for Intention Identification of the Korean Spoken Language

Abstract

Phonetic features are indispensable in understanding the spoken language. Especially in Korean, which is wh-in-situ and head-final, the addressee of spoken language sometimes finds it hard to discern the speaker’s original intention if not provided with the sentence prosody. However, acoustic information may not be guaranteed for all spoken language processing, due to the difficulty of managing and computing speech data. This article suggests a corpus that aims to distinguish utterances with ambiguous intention from clear-cut ones, utilizing the prosodic ambiguity of the text input. In detail, the resulting classification system decides whether the given text input is one of fragment, statement, question, command, rhetorical question/command, or indecisive, taking into account the intonation-dependency of the text. Based on an intuitive understanding of the Korean language engaged in the data annotation, we construct a corpus with seven intention categories, train classification systems, and validate the utility of our dataset with quantitative and qualitative analyses.