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The paper introduces a meta‑study that reviews state‑of‑the‑art end‑to‑end argument mining (AM) pipelines. It proposes a triple‑perspective framework—linguistic, computational, and domain—to analyze how these pipelines model, compute, and incorporate domain knowledge into argument structures. The authors also outline a general design for the linguistic and computational aspects, aiming to standardize methodology descriptions and enable clearer comparisons among AM approaches.
arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.
arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.
Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying...
The paper introduces DNE‑ElecDeb, an enriched version of the USElecDeb dataset that annotates Debate Named Entities (DNEs) in both argumentative and non‑argumentative spans, and defines Debate Named Entity Recognition (DNER) as a new task. It proposes Joint Argument and Entity Tagging (JAET), a generative framework that fine‑tunes decoder‑only LLMs to insert inline argument and entity tags into debate turns while preserving the original transcript. JAET achieves significant improvements in joint AM+DNER performance (+27.3% relative F1 in the untyped setting and +41.9% in the typed setting) over sequential pipelines, and these gains generalize to Persuasive Essays (+26.6% and +52.7%).
The paper introduces ContraTalk, a benchmark that tests whether dialogue models truly use acoustic cues or rely on transcript shortcuts. It formalizes cross‑modal disagreement, creates conflict and consistent QA examples, and proposes an Audio Twin representation to expose acoustic evidence to models. Experiments show that while text‑only LLMs perform well on consistent cases, they falter on conflict cases, and AudioLLMs only partially mitigate this issue.