@inproceedings{mahmud-etal-2025-mind,
title = "Mind{\_}{M}atrix at {CQ}s-Gen 2025: Adaptive Generation of Critical Questions for Argumentative Interventions",
author = "Mahmud, Sha Newaz and
Hossain, Shahriar and
Rahman, Samia and
Arefin Labib, Momtazul and
Murad, Hasan",
editor = "Chistova, Elena and
Cimiano, Philipp and
Haddadan, Shohreh and
Lapesa, Gabriella and
Ruiz-Dolz, Ramon",
booktitle = "Proceedings of the 12th Argument mining Workshop",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.argmining-1.32/",
doi = "10.18653/v1/2025.argmining-1.32",
pages = "332--339",
ISBN = "979-8-89176-258-9",
abstract = "To encourage computational argumentation through critical question generation (CQs-Gen),we propose an ACL 2025 CQs-Gen shared task system to generate critical questions (CQs) with the best effort to counter argumentative text by discovering logical fallacies, unjustified assertions, and implicit assumptions.Our system integrates a quantized language model, semantic similarity analysis, and a meta-evaluation feedback mechanism including the key stages such as data preprocessing, rationale-augmented prompting to induce specificity, diversity filtering for redundancy elimination, enriched meta-evaluation for relevance, and a feedback-reflect-refine loop for iterative refinement. Multi-metric scoring guarantees high-quality CQs. With robust error handling, our pipeline ranked 7th among 15 teams, outperforming baseline fact-checking approaches by enabling critical engagement and successfully detecting argumentative fallacies. This study presents an adaptive, scalable method that advances argument mining and critical discourse analysis."
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%0 Conference Proceedings
%T Mind_Matrix at CQs-Gen 2025: Adaptive Generation of Critical Questions for Argumentative Interventions
%A Mahmud, Sha Newaz
%A Hossain, Shahriar
%A Rahman, Samia
%A Arefin Labib, Momtazul
%A Murad, Hasan
%Y Chistova, Elena
%Y Cimiano, Philipp
%Y Haddadan, Shohreh
%Y Lapesa, Gabriella
%Y Ruiz-Dolz, Ramon
%S Proceedings of the 12th Argument mining Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-258-9
%F mahmud-etal-2025-mind
%X To encourage computational argumentation through critical question generation (CQs-Gen),we propose an ACL 2025 CQs-Gen shared task system to generate critical questions (CQs) with the best effort to counter argumentative text by discovering logical fallacies, unjustified assertions, and implicit assumptions.Our system integrates a quantized language model, semantic similarity analysis, and a meta-evaluation feedback mechanism including the key stages such as data preprocessing, rationale-augmented prompting to induce specificity, diversity filtering for redundancy elimination, enriched meta-evaluation for relevance, and a feedback-reflect-refine loop for iterative refinement. Multi-metric scoring guarantees high-quality CQs. With robust error handling, our pipeline ranked 7th among 15 teams, outperforming baseline fact-checking approaches by enabling critical engagement and successfully detecting argumentative fallacies. This study presents an adaptive, scalable method that advances argument mining and critical discourse analysis.
%R 10.18653/v1/2025.argmining-1.32
%U https://aclanthology.org/2025.argmining-1.32/
%U https://doi.org/10.18653/v1/2025.argmining-1.32
%P 332-339
Markdown (Informal)
[Mind_Matrix at CQs-Gen 2025: Adaptive Generation of Critical Questions for Argumentative Interventions](https://aclanthology.org/2025.argmining-1.32/) (Mahmud et al., ArgMining 2025)
ACL