DEBISS: a Corpus of Individual, Semi-structured and Spoken Debates

TL;DR

The paper introduces DEBISS, a new corpus of spoken, semi-structured debates with diverse NLP annotations, addressing the scarcity of debate datasets for various applications.

Contribution

It presents a comprehensive debate corpus with multi-faceted annotations, facilitating research in speech processing, argument mining, and speaker analysis.

Findings

DEBISS includes speech-to-text and speaker diarization annotations.

The corpus supports argument mining and debater quality assessment.

It addresses the lack of diverse debate corpora in NLP research.

Abstract

The process of debating is essential in our daily lives, whether in studying, work activities, simple everyday discussions, political debates on TV, or online discussions on social networks. The range of uses for debates is broad. Due to the diverse applications, structures, and formats of debates, developing corpora that account for these variations can be challenging, and the scarcity of debate corpora in the state of the art is notable. For this reason, the current research proposes the DEBISS corpus: a collection of spoken and individual debates with semi-structured features. With a broad range of NLP task annotations, such as speech-to-text, speaker diarization, argument mining, and debater quality assessment.