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‎_config.yml‎

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# Site settings
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# -----------------------------------------------------------------------------
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title: blank # the website title (if blank, full name will be used instead)
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first_name: You
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middle_name: R.
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last_name: Name
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title: Language Technologies Lab # the website title (if blank, full name will be used instead)
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first_name: Language Technologies Lab UniBo
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middle_name:
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last_name:
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contact_note: >
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You can even add a little note about which of these is the best way to reach you.
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description: > # the ">" symbol means to ignore newlines until "footer_text:"
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A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design.
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footer_text: >
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Powered by <a href="https://jekyllrb.com/" target="_blank">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio">al-folio</a> theme.
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Hosted by <a href="https://pages.github.com/" target="_blank">GitHub Pages</a>.
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Photos from <a href="https://unsplash.com" target="_blank">Unsplash</a>.
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keywords: jekyll, jekyll-theme, academic-website, portfolio-website # add your own keywords or leave empty
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lang: en # the language of your site (for example: en, fr, cn, ru, etc.)
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icon: ⚛️ # the emoji used as the favicon (alternatively, provide image name in /assets/img/)
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limit: 5 # leave blank to include all the news in the `_news` folder
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latest_posts:
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enabled: true
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enabled: false
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scrollable: true # adds a vertical scroll bar if there are more than 3 new posts items
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limit: 3 # leave blank to include all the blog posts
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‎_includes/figure.html‎

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{%- assign img_path = include.path | remove: ".jpg" | remove: ".jpeg" | remove: ".png" | remove: ".tiff" -%}
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<figure>
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<picture>
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{% if site.imagemagick.enabled %}
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{% for i in site.imagemagick.widths -%}
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<source
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class="responsive-img-srcset"
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media="(max-width: {{ i }}px)"
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srcset="{{ img_path | relative_url }}-{{ i }}.webp"
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/>
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{% endfor -%}
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{% endif %}
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<!-- Fallback to the original file -->
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<img
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src="{{ include.path | relative_url }}"
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{% if include.class %}class="{{ include.class }}"{% endif %}
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{% if include.width %}width="{{ include.width }}"{% else %}width="auto"{% endif %}
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{% if include.height %}height="{{ include.height }}"{% else %}height="auto"{% endif %}
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{% if include.min-width %}min-width="{{ include.min-width }}"{% endif %}
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{% if include.min-height %}min-height="{{ include.min-height }}"{% endif %}
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{% if include.max-width %}max-width="{{ include.max-width }}"{% endif %}
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{% if include.max-height %}height="{{ include.max-height }}"{% endif %}
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{% if include.alt %}alt="{{ include.alt }}"{% endif %}
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{% if include.title %}title="{{ include.title }}"{% endif %}
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{% if include.zoomable %}data-zoomable{% endif %}
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onerror="this.onerror=null; $('.responsive-img-srcset').remove();"
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/>
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</picture>
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{%- if include.caption -%}<figcaption class="caption">{{ include.caption }}</figcaption>{%- endif %}
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</figure>

‎_includes/people_horizontal.html‎

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<div class="card-item col">
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<a href="{{ include.url | relative_url }}">
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<div class="card hoverable">
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<div class="row g-0">
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<div class="card-img col-sm-4">
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{% include figure.html path=include.img alt=include.name %}
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</div>
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<div class="col-sm-6">
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<div class="card-body">
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<h5 class="card-title text-right">{{ include.name }}</h5>
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<p class="card-text text-right">{{ include.affiliation }}</p>
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</div>
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</div>
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</div>
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</div>
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</a>
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</div>

‎_pages/about.md‎

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layout: about
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title: about
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permalink: /
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subtitle: <a href='#'>Affiliations</a>. Address. Contacts. Motto. Etc.
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subtitle: <a href='https://www.unibo.it/en/'>Univerity of Bologna, Italy</a>
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# profile:
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# align: right
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# image: prof_pic.jpg
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# image_circular: false # crops the image to make it circular
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# more_info: >
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# <p>555 your office number</p>
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# <p>123 your address street</p>
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# <p>Your City, State 12345</p>
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profile:
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align: right
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image: prof_pic.jpg
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image_circular: false # crops the image to make it circular
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more_info: >
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<p>555 your office number</p>
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<p>123 your address street</p>
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<p>Your City, State 12345</p>
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news: true # includes a list of news items
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selected_papers: true # includes a list of papers marked as "selected={true}"
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social: true # includes social icons at the bottom of the page
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news: false # includes a list of news items
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selected_papers: false # includes a list of papers marked as "selected={true}"
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social: false # includes social icons at the bottom of the page
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---
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Write your biography here. Tell the world about yourself. Link to your favorite [subreddit](http://reddit.com). You can put a picture in, too. The code is already in, just name your picture `prof_pic.jpg` and put it in the `img/` folder.
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Put your address / P.O. box / other info right below your picture. You can also disable any of these elements by editing `profile` property of the YAML header of your `_pages/about.md`. Edit `_bibliography/papers.bib` and Jekyll will render your [publications page](/al-folio/publications/) automatically.
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Link to your social media connections, too. This theme is set up to use [Font Awesome icons](https://fontawesome.com/) and [Academicons](https://jpswalsh.github.io/academicons/), like the ones below. Add your Facebook, Twitter, LinkedIn, Google Scholar, or just disable all of them.
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* 🌍 We are based in Italy
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* 🔭 We are a research group of the Department of Computer Science and Engineering of the University of Bologna. Our focus is Natural Language Processing research and application. We contribute to several national and international research projects and offer a variety of NLP learning activities at the international [Master's Degree in Artificial Intelligence](https://corsi.unibo.it/2cycle/artificial-intelligence) and elsewhere.
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* 👤Members
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* [Paolo Torroni](https://www.unibo.it/sitoweb/p.torroni/en) - Head of the Language Technologies Lab
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* [Marco Lippi](http://personale.unimore.it/rubrica/dettaglio/marco.lippi)
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* [Andrea Galassi](https://www.unibo.it/sitoweb/a.galassi/en)
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* [Federico Ruggeri](https://www.unibo.it/sitoweb/federico.ruggeri6/en)
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* [Eleonora Mancini](https://www.unibo.it/sitoweb/e.mancini/en)
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* [Elena Palmieri](https://www.unibo.it/sitoweb/e.palmieri/en)
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* [Giulia Grundler](https://www.unibo.it/sitoweb/giulia.grundler2/en)
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* [Francesco Antici](https://www.unibo.it/sitoweb/francesco.antici/en)
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* [Gianmarco Pappacoda](https://www.unibo.it/sitoweb/gianmarco.pappacoda/en)
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* [Nicolò Donati](https://www.unibo.it/sitoweb/n.donati/en)
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* [Rooshan Saleem Butt](https://www.unibo.it/sitoweb/rooshan.saleembutt/en)
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* [Luca Bolognini](https://www.unibo.it/sitoweb/luca.bolognini6)

‎_pages/blog.md‎

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layout: default
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permalink: /blog/
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title: blog
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nav: true
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nav: false
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nav_order: 1
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pagination:
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enabled: true

‎_pages/cv.md‎

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layout: cv
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permalink: /cv/
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title: cv
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nav: true
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nav: false
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nav_order: 5
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cv_pdf: example_pdf.pdf # you can also use external links here
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description: This is a description of the page. You can modify it in '_pages/cv.md'. You can also change or remove the top pdf download button.

‎_pages/dropdown.md‎

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---
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layout: page
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title: submenus
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nav: true
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nav: false
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nav_order: 8
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dropdown: true
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children:

‎_pages/mm-argfallacy.md‎

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---
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layout: page
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title: MM-ArgFallacy Shared Task
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permalink: /mm-argfallacy/
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description: Page of MM-ArgFallacy Shared Task.
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nav: false
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#nav_order: 8
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related_publications: false
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---
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<!-- pages/parkinsons-speech-xai.md -->
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‎_pages/mm-argfallacy2025.md‎

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---
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layout: page
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title: MM-ArgFallacy2025
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permalink: /mm-argfallacy/2025/
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description:
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nav: false
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#nav_order: 8
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related_publications: false
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---
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<!-- pages/parkinsons-speech-xai.md -->
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Multimodal Argumentative Fallacy Detection and Classification on Political Debates Shared Task.
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Co-located with The [12th Workshop on Argument Mining](https://argmining-org.github.io/2025/) in Vienna, Austria.
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# Overview
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This shared task focuses on detecting and classifying fallacies in **political debates** by integrating text and audio data. Participants will tackle two sub-tasks:
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- **Argumentative Fallacy Detection**
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- **Argumentative Fallacy Classification**
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We offer three input settings:
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- **Text-only:** Analyze textual arguments.
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- **Audio-only:** Explore paralinguistic features.
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- **Text + Audio:** Combine both for a multimodal perspective.
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Join us to advance multimodal argument mining and uncover new insights into human reasoning! 💬
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# Tasks
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**Task A**
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- **Input**: a sentence, in the form of text or audio or both, extracted from a political debate.
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- **Task**: to determine whether the input contains an argumentative fallacy.
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**Task B**
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- **Input**: a sentence, in the form of text or audio or both, extracted from a political debate, containing a fallacy.
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- **Task**: to determine the type of fallacy contained in the input, according to the classification introduced by [Goffredo et al. (2022)](https://www.ijcai.org/proceedings/2022/575). We only refer to macro categories.
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-----------------------------------
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For each sub-task, participants can leverage the debate context of a given input: all its previous sentences and corresponding aligned audio samples. For instance, consider the **text-only** input mode. Given a sentence from a political debate at index *i*, participants can use sentences with indexes from *0* to *i - 1*, where *0* denotes the first sentence in the debate.
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------------------------------------
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# Data
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We use **MM-USED-fallacy** and release a version of the dataset specifically designed for argumentative fallacy detection. This dataset includes 1,891 sentences from [Haddadan et al.'s (2019)](https://aclanthology.org/P19-1463.pdf) dataset on US presidential elections. Each sentence is labeled with one of six argumentative fallacy categories, as introduced by [Goffredo et al. (2022)](https://www.ijcai.org/proceedings/2022/575).
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Inspired by observations from [Goffredo et al. (2022)](https://www.ijcai.org/proceedings/2022/575) on the benefits of leveraging multiple argument mining tasks for fallacy detection and classification, we also provide additional datasets to encourage multi-task learning. A summary is provided in the table below:
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For argumentative fallacy detection, we will compute the binary F1-score on predicted sentence-level labels.
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For argumentative fallacy classification, we will compute the macro F1-score on predicted sentence-level labels.
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Metrics will be computed on the hidden test set to determine the best system for each sub-task and input mode.
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---
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| **Dataset** | **Description** | **Size** |
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|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|
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| **UKDebates** | 386 sentences and audio samples from the 2015 UK Prime Ministerial elections. Sentences are labeled for argumentative sentence detection: containing or not containing a claim. | 386 sentences |
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| **M-Arg** | A multimodal dataset for argumentative relation classification from the 2020 US Presidential elections. Sentences are labeled as attacking, supporting, or unrelated to another sentence. | 4,104 pairs |
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| **MM-USED** | A multimodal extension of the USElecDeb60to16 dataset, covering US presidential debates (1960–2016). Includes labels for argumentative sentence detection and component classification. | 26,781 sentences |
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---
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All datasets will be available through [MAMKit](https://nlp-unibo.github.io/mamkit/).
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Since many multimodal datasets cannot release audio samples due to copyright restrictions, MAMKit provides an interface to dynamically build datasets and promote reproducible research.
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Datasets are formatted as `torch.Dataset` objects, containing input values (text, audio, or both) and corresponding task-specific labels. More details about data formats and dataset building are available in MAMKit's documentation.
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# Evaluation
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For argumentative fallacy detection, we will compute the binary F1-score on predicted sentence-level labels.
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For argumentative fallacy classification, we will compute the macro F1-score on predicted sentence-level labels.
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Metrics will be computed on the hidden test set to determine the best system for each sub-task and input mode.
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# Key Dates (Anywhere on Earth)
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Will be updated soon.
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# Submission
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We be updated soon.
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# Task Organizers
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<div class="row row-cols-2 projects pt-3 pb-3">
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{% include people_horizontal.html name="Eleonora Mancini" affiliation="Language Technologies Lab, University of Bologna, Italy" url="https://helemanc.github.io/" img="/assets/img/people/eleonora_mancini.jpeg" %}
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{% include people_horizontal.html name="Federico Ruggeri" affiliation="Language Technologies Lab, University of Bologna, Italy" url="https://www.unibo.it/sitoweb/federico.ruggeri6" img="/assets/img/people/fede.png" %}
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{% include people_horizontal.html name="Paolo Torroni" affiliation="Language Technologies Lab, University of Bologna, Italy" url="https://www.unibo.it/sitoweb/p.torroni/en" img="/assets/img/people/paolo.png" %}
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{% include people_horizontal.html name="Serena Villata" affiliation="Inria-I3S WIMMICS Laboratoire I3S, CNRS, Sophia Antipolis, France" url="https://webusers.i3s.unice.fr/~villata/Home.html" img="assets/img/people/serena_villata.jpg" %}
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</div>
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# Contacts
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**[Join the MM-ArgFallacy2025 Slack Channel!](https://join.slack.com/t/mm-argfallacy2025/shared_invite/zt-2yjct5udc-vbuGSsSelR5FMiopSne~wQ)**

‎_pages/profiles.md‎

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permalink: /people/
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title: people
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description: members of the lab or group
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nav: true
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nav: false
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nav_order: 7
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profiles:

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