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

Klaywert Danillo Ferreira de Souza; David Eduardo Pereira; Cláudio E. C. Campelo; Larissa Lucena Vasconcelos

cs.CLMar 5, 2026

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

Authors: Klaywert Danillo Ferreira de Souza, David Eduardo Pereira, Cláudio E. C. Campelo, Larissa Lucena Vasconcelos

Organizations: Federal University of Campina Grande (UFCG) - System and Computing Campina Grande, Brazil

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.

Figures & tables

Figure 1: Room layout during debate sessions

Stats	Value
Number of debaters	67
Number of groups	16
Total audio length of recordings	9 hours and 37 minutes
Number of tokens	130697
Words lexical diversity 5 5 5 Measure of vocabulary variation within a text	0.062

Table 1: DEBISS stats numbers

Figure 3: Illustrative Example of DEBISS-Arg Data (extracted from [omitted])

Figure 4: Illustrative Example of judges notes on a argument in DEBISS-eval

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Sep 18, 2026J/K move · Enter open · S save

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LLM Evaluation

Open paper AskPDF arXiv

Debate adjudication requires tracking how arguments develop through interaction, yet existing datasets rarely combine fine-grained debate transcripts with professional judgments collected during real competitions under a shared rubric. We introduce a dataset and benchmark for evaluating large language models' understanding of competitive Chinese-language debate at the match, stage, and speaker levels. We organized 182 matches and recruited 120 professional judges, with each match independently adjudicated by three judges using a predefined rubric. After excluding matches with incomplete records, the dataset contains 148 matches, 2,698 stages, and 20,542 exchange units, with manually verified transcripts and segmentation. It preserves original stage scores, match votes, best-debater ballots, and adjudication rationales. We define three tasks: winner-tendency prediction, stage-score prediction, and best-debater prediction. Zero-shot evaluation of multiple large language models yields a highest winner-prediction accuracy of 66.2%, a highest Pearson correlation of 0.250 between model stage scores and mean human ratings, and a highest best-debater prediction accuracy of 56.8%. The dataset and benchmark provide a testbed for studying large language models' understanding of interactive argumentation and their agreement with professional judges.

Abstract

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