arXiv Computation and Language
Oct 21

From DQI to C-DQI: Automatic Codebook Generation and Evaluation for Measuring Computational Discourse Quality Index

The paper introduces a protocol for generating and evaluating codebooks for measuring Computational Discourse Quality Index (DQI) using Large Language Models (LLMs). It compares top‑down, bottom‑up, and hybrid approaches, finding that inductive, data‑driven schemas yield higher inter‑model agreement while remaining valid. Experiments on 100 Israeli parliament transcripts support the reliability of these data‑driven schemas for large‑scale political discourse analysis.

By Esther Shizgal, Ron Keinan, Daniel Shtauber, Roee Tzvi Esquira, Ori Aronson, Matan Toledano, Or Rappel-Kroyzer, Liron Lavi, Renana Keydar, Ittai Bar-Siman-Tov, Reut Tsarfaty
arXiv AI
Jun 17

RooseBERT: A New Deal For Political Language Modelling

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.

By Deborah Dore, Elena Cabrio, Serena Villata
arXiv Computation and Language
Oct 23

Interpretive Variation and Discourse Structure in Detecting Enthymemes in Controversial Political Discourse

The paper investigates how to detect enthymemes—arguments with unstated premises or conclusions—in controversial political tweets about vaccination and immigration. Despite extensive fine‑tuning, models achieve an F1 score of only about 0.67, prompting the authors to propose new evaluation metrics that compare predictions to the full distribution of annotator votes and a model that directly predicts this distribution. The study also shows that models rely on explicit argumentative structure, often missing implicit links that readers must infer, raising concerns for content moderation systems that depend on majority labels.

By Martial Pastor, Nelleke Oostdijk
arXiv AI
Aug 24

Trilingual Topic Modeling of Sri Lankan Parliamentary Debates

The paper presents an end‑to‑end framework for extracting and clustering trilingual Sri Lankan parliamentary debates in Sinhala, Tamil, and English. Using LLM‑based text extraction, multilingual embeddings, and density‑based clustering, the authors recover 30 macro‑topics with a cluster purity of 0.673. The temporal patterns of these topics align with major national events such as the 2019 Easter attacks and the 2022 economic crisis, demonstrating the method’s effectiveness where traditional LDA fails.

By Himath Dhanapala, Haren Daishika, Himandhi Kuruppu, Sithija Seneviratne, Ashini Kavindya, Patalee Narasinghe, Sandeepa Weerasekara, Nisansa de Silva, Sandareka Wickramanayake