<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Argument Mining | Language Technologies Lab</title><link>http://nlp.unibo.it/proposals_am/</link><atom:link href="http://nlp.unibo.it/proposals_am/index.xml" rel="self" type="application/rss+xml"/><description>Argument Mining</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 02 Mar 2026 00:00:00 +0000</lastBuildDate><image><url>http://nlp.unibo.it/media/icon_hu_7613a4a452ac7087.png</url><title>Argument Mining</title><link>http://nlp.unibo.it/proposals_am/</link></image><item><title>Hate Speech Detection with Argumentative Reasoning</title><link>http://nlp.unibo.it/proposals_am/hatespeech/</link><pubDate>Mon, 02 Mar 2026 00:00:00 +0000</pubDate><guid>http://nlp.unibo.it/proposals_am/hatespeech/</guid><description>&lt;p>&lt;strong>Description:&lt;/strong>&lt;br>
Hate speech often lies on implicit content and subtle reasoning nuances.
Our idea is to apply argumentative reasoning to hate speech to make implicit content explicit in order to define more interpretable and user-friendly hate speech detection systems.&lt;/p>
&lt;p>&lt;strong>Contact:&lt;/strong> &lt;a href="mailto:federico.ruggeri6@unibo.it">Federico Ruggeri&lt;/a>, &lt;a href="mailto:arianna.muti@unibocconi.it">Arianna Muti&lt;/a>&lt;/p>
&lt;p>&lt;strong>References:&lt;/strong>&lt;/p>
&lt;p>&lt;strong>Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts&lt;/strong>&lt;br>
Arianna Muti, Federico Ruggeri, Khalid Al-Khatib, Alberto Barrón-Cedeño, Tommaso Caselli&lt;br>
Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 21091–21107, 2024&lt;br>
&lt;a href="https://doi.org/10.18653/v1/2024.emnlp-main.1174" target="_blank" rel="noopener">DOI&lt;/a>
| &lt;a href="https://aclanthology.org/2024.emnlp-main.1174.pdf" target="_blank" rel="noopener">PDF&lt;/a>&lt;/p>
&lt;p>&lt;strong>PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets&lt;/strong>&lt;br>
Arianna Muti, Federico Ruggeri, Cagri Toraman, Lorenzo Musetti, Samuel Algherini, Silvia Ronchi, Gianmarco Saretto, Caterina Zapparoli, Alberto Barrón-Cedeño.&lt;br>
In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12700–12711, Torino, Italia. ELRA and ICCL.&lt;br>
&lt;a href="https://aclanthology.org/2024.lrec-main.1112.pdf" target="_blank" rel="noopener">PDF&lt;/a>
| &lt;a href="https://aclanthology.org/2024.lrec-main.1112" target="_blank" rel="noopener">Anthology&lt;/a>&lt;/p></description></item><item><title>Multimodal Argument Mining</title><link>http://nlp.unibo.it/proposals_am/mam/</link><pubDate>Mon, 02 Mar 2026 00:00:00 +0000</pubDate><guid>http://nlp.unibo.it/proposals_am/mam/</guid><description>&lt;p>&lt;strong>Description:&lt;/strong>&lt;br>
Make use of speech information (e.g. prosody) to enhance the set of features that can be used to detect arguments.
Speech can either be represented by means of ad-hoc feature extraction methods (e.g. MFCC) or via end-to-end architectures.
Few existing corpora both offer argument annotation layers and speech data regarding a given text document.&lt;/p>
&lt;p>&lt;strong>Contact:&lt;/strong> &lt;a href="mailto:e.mancini@unibo.it">Eleonora Mancini&lt;/a>, &lt;a href="mailto:federico.ruggeri6@unibo.it">Federico Ruggeri&lt;/a>&lt;/p>
&lt;p>&lt;strong>References:&lt;/strong>&lt;/p>
&lt;p>&lt;strong>MAMKit: A Comprehensive Multimodal Argument Mining Toolkit.&lt;/strong>&lt;br>
Eleonora Mancini, Federico Ruggeri, Stefano Colamonaco, Andrea Zecca, Samuele Marro, and Paolo Torroni. 2024.&lt;br>
In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 69–82, Bangkok, Thailand. Association for Computational Linguistics.&lt;br>
&lt;a href="https://doi.org/10.18653/v1/2024.argmining-1.7" target="_blank" rel="noopener">DOI&lt;/a>
| &lt;a href="https://aclanthology.org/2024.argmining-1.7.pdf" target="_blank" rel="noopener">PDF&lt;/a>&lt;/p>
&lt;p>&lt;strong>Multimodal Fallacy Classification in Political Debates&lt;/strong>&lt;br>
Eleonora Mancini, Federico Ruggeri, Paolo Torroni&lt;br>
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 170–178, 2024&lt;br>
&lt;a href="https://doi.org/10.18653/v1/2024.eacl-short.16" target="_blank" rel="noopener">DOI&lt;/a>
| &lt;a href="https://aclanthology.org/2024.eacl-short.16.pdf" target="_blank" rel="noopener">PDF&lt;/a>&lt;/p>
&lt;p>&lt;strong>Multimodal Argument Mining: A Case Study in Political Debates&lt;/strong>&lt;br>
Eleonora Mancini, Federico Ruggeri, Andrea Galassi, and Paolo Torroni.&lt;br>
In Proceedings of the 9th Workshop on Argument Mining, pages 158–170, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics, 2022.&lt;br>
&lt;a href="https://aclanthology.org/2022.argmining-1.15.pdf" target="_blank" rel="noopener">PDF&lt;/a>
| &lt;a href="https://aclanthology.org/2022.argmining-1.15" target="_blank" rel="noopener">Anthology&lt;/a>&lt;/p></description></item></channel></rss>