mlsanityData to 14 Oct 2026

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

arXiv 2302.12584 ↗First posted 24 Feb 2023cs.CLcited by 4

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Abstract (arXiv)

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

5 of 26 references.

References · 5

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2022SHAS: Approaching optimal Segmentation for End-to-End Speech Translationcited by 26
  4. 2020Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluationcited by 20
  5. 2022A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Studycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.