Argument Mining | Language Technologies Lab

Argument Mining on Legal Datasets

Mon, 02 Mar 2026 00:00:00 +0000

Description:
Argumentation in legal documents is typically well-structured and follows specific domain rules. We are interested in applying both the most recent NLP techniques and also hybrid techniques that can leverage the domain knowledge.

Contact: Andrea Galassi, Marco Lippi

Hate Speech Detection with Argumentative Reasoning

Mon, 02 Mar 2026 00:00:00 +0000

Description:
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.

Contact: Federico Ruggeri, Arianna Muti

References:

Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts
Arianna Muti, Federico Ruggeri, Khalid Al-Khatib, Alberto Barrón-Cedeño, Tommaso Caselli
Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 21091–21107, 2024
DOI | PDF

PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets
Arianna Muti, Federico Ruggeri, Cagri Toraman, Lorenzo Musetti, Samuel Algherini, Silvia Ronchi, Gianmarco Saretto, Caterina Zapparoli, Alberto Barrón-Cedeño.
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.
PDF | Anthology

Multimodal Argument Mining

Mon, 02 Mar 2026 00:00:00 +0000

Description:
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.

Contact: Eleonora Mancini, Federico Ruggeri

References:

MAMKit: A Comprehensive Multimodal Argument Mining Toolkit.
Eleonora Mancini, Federico Ruggeri, Stefano Colamonaco, Andrea Zecca, Samuele Marro, and Paolo Torroni. 2024.
In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 69–82, Bangkok, Thailand. Association for Computational Linguistics.
DOI | PDF

Multimodal Fallacy Classification in Political Debates
Eleonora Mancini, Federico Ruggeri, Paolo Torroni
18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 170–178, 2024
DOI | PDF

Multimodal Argument Mining: A Case Study in Political Debates
Eleonora Mancini, Federico Ruggeri, Andrea Galassi, and Paolo Torroni.
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.
PDF | Anthology

AMELR

Fri, 27 Feb 2026 00:00:00 +0000

The AMELR workshop focuses on Legal Argument Mining (LAM) - using NLP to automatically detect legal arguments. Recent developments in NLP and LAM have provided legal scholars with a powerful tool for studying reasoning patterns, interpretative theories, and biases across jurisdictions and legal systems. The workshop gathers experts in computer science, AI & Law, legal theory, and empirical legal studies to address key challenges of LAM: creating training datasets, developing reliable models, establishing reproducibility standards, and integrating LAM into legal research. The workshop aims to strengthen the emerging field of LAM and its role in empirical legal studies by sharing latest implementations, addressing core challenges, and establishing best practices.

ArgMining

Fri, 27 Feb 2026 00:00:00 +0000

Argument mining (also known as “argumentation mining”) is a well-established research area in computational linguistics that focuses on the automatic identification of argumentative structures, such as premises, conclusions, and inference schemes. Since its beginnings, the focus has been on the development of large-scale argumentation dataset and tasks like argument quality assessment, argument persuasiveness, and the synthesis of argumentative texts, spanning various domains, such as legal, social, medical, political, and scientific settings.

Argument Mining

Fri, 27 Feb 2026 00:00:00 +0000

Argumentative Fallacies

The detection and classification of argumentative fallacies. Fallacy constitute an important aspect of argumentation because they require substantial reasoning capabilities to be spotted, thus, representing a valuable challenge for machine learning models.

Multimodality

We have worked in evaluating the combination of audio and text modalities. Does audio modality provide any benefit in addressing argument mining tasks?

Dialogues

We have developed a benchmark for argumentative dialogues on scientific papers. Nonetheless, nowadays, the focus is more on developing commercial tools rather than on developing benchmarks.

Interpretability

The development of interpretable models for argumentation. A current focus is on extracting local explanations from texts to acquire insights about data. For instance, which kind of patterns are associated with certain argumentative components?

Reasoning in LLMs

LLMs and reasoning are hot topics that are currently vastly being investigated. Though, there are still few attempts that aim to use argumentation as a resource for assessing reasoning in LLMs.

The 13th Workshop on Argument Mining and Reasoning Co-located with ACL 2026

Wed, 01 Oct 2025 00:00:00 +0000

Introduction

The 2026 edition of the ArgMining workshop therefore places a special focus on understanding and evaluating arguments in both human and machine reasoning. With this topic, we broaden the workshop’s focus to include reasoning, a long-standing area of research in AI that has recently gained renewed interest within the *ACL community, driven by the latest generation of LLMs. Reasoning is tightly connected to argumentation as it represents, analyzes and evaluates the process of reaching conclusions on the basis of available information. If we consider argumentation as a paradigm to capture reasoning, then machines (particularly LLMs) can be evaluated based on their ability to address argument mining tasks.

The workshop will be co-located with ACL 2026 and held in San Diego, United States in a hybrid format.

Useful Links

Overview of MM-ArgFallacy2025 on Multimodal Argumentative Fallacy Detection and Classification in Political Debates

Tue, 01 Jul 2025 00:00:00 +0000

Multimodal Argumentative Fallacy Detection and Classification on Political Debates Shared Task.

Co-located with The 12th Workshop on Argument Mining in Vienna, Austria.

Overview

This shared task focuses on detecting and classifying fallacies in political debates by integrating text and audio data. Participants will tackle two sub-tasks:

Argumentative Fallacy Detection
Argumentative Fallacy Classification

We offer three input settings:

Text-only: Analyze textual arguments.
Audio-only: Explore paralinguistic features.
Text + Audio: Combine both for a multimodal perspective.

Join us to advance multimodal argument mining and uncover new insights into human reasoning! 💬

Tasks

Task A

Input: a sentence, in the form of text or audio or both, extracted from a political debate.
Task: to determine whether the input contains an argumentative fallacy.

Task B

Input: a sentence, in the form of text or audio or both, extracted from a political debate, containing a fallacy.
Task: to determine the type of fallacy contained in the input, according to the classification introduced by Goffredo et al. (2022). We only refer to macro categories.

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.

Data

We use MM-USED-fallacy and release a version of the dataset specifically designed for argumentative fallacy detection. This dataset includes 1,278 sentences from Haddadan et al.’s (2019) dataset on US presidential elections. Each sentence is labeled with one of six argumentative fallacy categories, as introduced by Goffredo et al. (2022).

Inspired by observations from Goffredo et al. (2022) 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:

Dataset	Description	Size
MM-USED-fallacy	A multimodal extension of USElecDeb60to20 dataset, covering US presidential debates (1960-2020). Inlcludes labels for argumentative fallacy detection and argumentative fallacy classification.	1,278 samples (updated version)
MM-USED	A multimodal extension of the USElecDeb60to16 dataset, covering US presidential debates (1960–2016). Includes labels for argumentative sentence detection and component classification.	23,505 sentences (updated version)
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
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

All datasets will be available through MAMKit.

Since many multimodal datasets cannot release audio samples due to copyright restrictions, MAMKit provides an interface to dynamically build datasets and promote reproducible research.

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. ## Retrieving the Data through MAMKit

To retrieve the datasets through MAMKit, you can use the following code interface:

from mamkit.data.datasets import MMUSEDFallacy, MMUSED, UKDebates, MArg, InputMode

import logging
from pathlib import Path

def loading_data_example():
 base_data_path = Path(__file__).parent.parent.resolve().joinpath('data')

 # MM-USED-fallacy dataset
 mm_used_fallacy_loader = MMUSEDFallacy(
 task_name='afc', # Choose between 'afc' or 'afd' 
 input_mode=InputMode.TEXT_AUDIO, # Choose between TEXT_ONLY, AUDIO_ONLY, or TEXT_AUDIO
 base_data_path=base_data_path
 )

 # MM-USED dataset
 mm_used_loader = MMUSED(
 task_name='asd',#Choose between 'asd' or 'acc' 
 input_mode=InputMode.TEXT_AUDIO, # Choose between TEXT_ONLY, AUDIO_ONLY, or TEXT_AUDIO
 base_data_path=base_data_path
 )

 # UKDebates dataset
 uk_debates_loader = UKDebates(
 task_name='asd',
 input_mode=InputMode.TEXT_AUDIO, # Choose between TEXT_ONLY, AUDIO_ONLY, or TEXT_AUDIO
 base_data_path=base_data_path
 )

 # M-Arg dataset
 m_arg_loader = MArg(
 task_name='arc',
 input_mode=InputMode.TEXT_AUDIO, # Choose between TEXT_ONLY, AUDIO_ONLY, or TEXT_AUDIO
 base_data_path=base_data_path
 )

Each loader is initialized with the appropriate task name (afc for argumentative fallacy classification, asd for argumentative sentence detection, and ‘arc’ for argumentative relation classification), input mode (InputMode.TEXT_ONLY, InputMode.AUDIO_ONLY, or InputMode.TEXT_AUDIO), and the base data path.

Ensure that you have MAMKit installed and properly configured in your environment to use these loaders.

For more details, refer to the MAMKit GitHub repository and website .

Test Set Access 🔍

The test set for mm-argfallacy-2025 is now available! To use it, please:

Create a fresh environment
Clone the repository and install the requirements:

git clone git@github.com:nlp-unibo/mamkit.git
cd mamkit
pip install -r requirements.txt
pip install --editable .

Access MAMKit in your Python code:

import mamkit

Then, retrieve the data using the following code:

For Fallacy Classification (`afc`):

from mamkit.data.datasets import MMUSEDFallacy, InputMode
from pathlib import Path

def loading_data_example():
 base_data_path = Path(__file__).parent.parent.resolve().joinpath('data')
 loader = MMUSEDFallacy(
 task_name='afc',
 input_mode=InputMode.TEXT_ONLY, # or TEXT_AUDIO or AUDIO_ONLY
 base_data_path=base_data_path
 )
 split_info = loader.get_splits('mm-argfallacy-2025')

For Fallacy Detection (`afd`):

from mamkit.data.datasets import MMUSEDFallacy, InputMode
from pathlib import Path

def loading_data_example():
 base_data_path = Path(__file__).parent.parent.resolve().joinpath('data')
 loader = MMUSEDFallacy(
 task_name='afd',
 input_mode=InputMode.TEXT_ONLY, # or TEXT_AUDIO or AUDIO_ONLY
 base_data_path=base_data_path
 )
 split_info = loader.get_splits('mm-argfallacy-2025')

References

MM-USED-fallacy: Mancini et al. (2024). The version provided through MAMKit includes updated samples, with refinements in the alignment process. This results in a different number of samples compared to the original dataset.
MM-USED: Mancini et al. (2022). The version provided through MAMKit includes updated samples, with refinements in the alignment process. This results in a different number of samples compared to the original dataset.
UK-Debates: Lippi and Torroni (2016).
M-Arg: Mestre et al. (2021).

Note: By “updated version,” we mean that the datasets have undergone a refinement in the alignment process, which has resulted in adjustments to the number of samples included compared to the original versions published in the referenced papers.

Evaluation

For argumentative fallacy detection, we will compute the binary F1-score on predicted sentence-level labels.
For argumentative fallacy classification, we will compute the macro F1-score on predicted sentence-level labels.
Metrics will be computed on the hidden test set to determine the best system for each sub-task and input mode.

Evaluation will be performed via the CodaLab platform.
On CodaLab, participants will find the leaderboard, along with the results of the provided baselines.
Submission guidelines can be found under the Evaluation section of the CodaLab competition page.

🚨 Important: In the evaluation website, you will also find a link to a mandatory participation survey.
Filling out this survey is required in order to participate in the task.
We also provide the survey link here for convenience: https://tinyurl.com/limesurvey-argfallacy

Baseline Results on Test Set

Argumentative Fallacy Classification (AFC) – Macro F1-score

Input Modality	Model	F1-Score
Text-only	BiLSTM w/ GloVe	47.21
Text-only	RoBERTa	39.25
Audio-only	BiLSTM w/ MFCCs	15.82
Audio-only	WavLM	6.43
Text + Audio	BiLSTM (GloVe + MFCCs)	21.91
Text + Audio	MM-RoBERTa + WavLM	38.16

Argumentative Fallacy Detection (AFD) – Binary F1-score

Input Modality	Model	F1-Score
Text-only	BiLSTM w/ GloVe	24.62
Text-only	RoBERTa	27.70
Audio-only	BiLSTM w/ MFCCs	0.00
Audio-only	WavLM	0.00
Text + Audio	BiLSTM (GloVe + MFCCs)	23.37
Text + Audio	MM-RoBERTa + WavLM	28.48

Submission

All evaluated submissions are required to commit to submitting a system description paper. You can choose between two options:

Non-Archival Paper:
A 2-page paper describing your system, with unlimited pages for appendices and bibliography. These papers will not be published in the workshop proceedings, but your system will be mentioned in the Overview Paper of the shared task, upon acceptance.
Archival Paper:
A 4-page paper describing your system, also with unlimited pages for appendices and bibliography. These papers will be published in the official ACL workshop proceedings and must be presented at the workshop (poster or oral session).
⚠️ In accordance with ACL policy, at least one team member must register for the workshop in order to present an archival paper if aaccepted to be published at the ACL proceedings.

All papers must use the official ACL style templates, available in both LaTeX and Word. We strongly recommend using the official Overleaf template for convenience.

We have sent an email to each team with all the details regarding the system description paper submission for MM-ArgFallacy2025. Please check your inbox (and spam folder just in case).

🗓️ Submissions open: May 1st, 2025 (the day after the end of the evaluation period)
🗓️ Submissions close: May 15th, 2025
📢 Notification of acceptance: May 20th, 2025
📝 Camera-ready deadline: May 25th, 2025

Important notes:

All accepted archival papers will be presented during the workshop’s poster session and require at least one registered author.
Non-archival papers do not require registration and are not presented at the workshop, but their systems will be acknowledged in the Overview Paper.

We look forward to receiving your submissions!

🏆 Leaderboard – Shared Task Results

`AFC Task – Argumentative Fallacy Classification`

📝 Text-only

Rank	Team	F1-Macro
1	Team NUST	0.4856
2	Baseline BiLSTM	0.4721
3	alessiopittiglio	0.4444
4	Baseline RoBERTa	0.3925
5	Team CASS	0.1432

🔊 Audio-only

Rank	Team	F1-Macro
1	alessiopittiglio	0.3559
2	Team NUST	0.1588
3	Baseline BiLSTM + MFCCs	0.1582
4	Team CASS	0.0864
5	Baseline WavLM	0.0643

🔁 Text-Audio

Rank	Team	F1-Macro
1	Team NUST	0.4611
2	alessiopittiglio	0.4403
3	Baseline RoBERTa + WavLM	0.3816
4	Baseline BiLSTM + MFCCs	0.2191
5	Team CASS	0.1432

`AFD Task – Argumentative Fallacy Detection`

📝 Text-only

Rank	Team	F1-Macro
1	Baseline RoBERTa	0.2770
2	Ambali_Yashovardhan	0.2534
3	Baseline BiLSTM	0.2462
4	Team EvaAdriana	0.2195

🔊 Audio-only

Rank	Team	F1-Macro
1	Ambali_Yashovardhan	0.2095
2	Team EvaAdriana	0.1690
3	Baseline BiLSTM + MFCCs	0.0000
4	Baseline WavLM	0.0000

🔁 Text-Audio

Rank	Team	F1-Macro
1	Baseline RoBERTa + WavLM	0.2848
2	Baseline BiLSTM + MFCCs	0.2337
3	Ambali_Yashovardhan	0.2244
4	Team EvaAdriana	0.1931

Key Dates (Anywhere on Earth)

Release of Training Data: February 25th
Release of Test Set: ~~March 24th~~ → April 7th
Evaluation Start: ~~April 14th~~ → April 21st
Evaluation End: ~~April 25th~~ → April 30th
Paper Submissions Open: May 1st
Paper Submission Close: May 15th
Notification of acceptance: May 20th
Camera-ready Due: May 25th
Workshop: July 31st

Task Organizers

	Eleonora Mancini Language Technologies Lab, University of Bologna, Italy		Federico Ruggeri Language Technologies Lab, University of Bologna, Italy
	Serena Villata Inria-I3S WIMMICS Laboratoire I3S, CNRS, Sophia Antipolis, France		Paolo Torroni Language Technologies Lab, University of Bologna, Italy

Contacts

Join the MM-ArgFallacy2025 Slack Channel!

Cite

Eleonora Mancini, Federico Ruggeri, Serena Villata, and Paolo Torroni. 2025. Overview of MM-ArgFallacy2025 on Multimodal Argumentative Fallacy Detection and Classification in Political Debates. In Proceedings of the 12th Argument mining Workshop, pages 358–368, Vienna, Austria. Association for Computational Linguistics.

@inproceedings{mancini-etal-2025-overview,
 title = "Overview of {MM}-{A}rg{F}allacy2025 on Multimodal Argumentative Fallacy Detection and Classification in Political Debates",
 author = "Mancini, Eleonora and
 Ruggeri, Federico and
 Villata, Serena and
 Torroni, Paolo",
 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-1.35/",
 doi = "10.18653/v1/2025.argmining-1.35",
 pages = "358--368",
 ISBN = "979-8-89176-258-9",
 abstract = "We present an overview of the MM-ArgFallacy2025 shared task on Multimodal Argumentative Fallacy Detection and Classification in Political Debates, co-located with the 12th Workshop on Argument Mining at ACL 2025. The task focuses on identifying and classifying argumentative fallacies across three input modes: text-only, audio-only, and multimodal (text+audio), offering both binary detection (AFD) and multi-class classification (AFC) subtasks. The dataset comprises 18,925 instances for AFD and 3,388 instances for AFC, from the MM-USED-Fallacy corpus on U.S. presidential debates, annotated for six fallacy types: Ad Hominem, Appeal to Authority, Appeal to Emotion, False Cause, Slippery Slope, and Slogan. A total of 5 teams participated: 3 on classification and 2 on detection. Participants employed transformer-based models, particularly RoBERTa variants, with strategies including prompt-guided data augmentation, context integration, specialised loss functions, and various fusion techniques. Audio processing ranged from MFCC features to state-of-the-art speech models. Results demonstrated textual modality dominance, with best text-only performance reaching 0.4856 F1-score for classification and 0.34 for detection. Audio-only approaches underperformed relative to text but showed improvements over previous work, while multimodal fusion showed limited improvements. This task establishes important baselines for multimodal fallacy analysis in political discourse, contributing to computational argumentation and misinformation detection capabilities."
}

Credits

This shared task is partially supported by the project European Commission's NextGeneration EU programme, PNRR -- M4C2 -- Investimento 1.3, Partenariato Esteso, PE00000013 - FAIR - Future Artificial Intelligence Research'' -- Spoke 8 Pervasive AI’’.

MAMKit: A Comprehensive Multimodal Argument Mining Toolkit

Thu, 01 Aug 2024 00:00:00 +0000

Add the full text or supplementary notes for the publication here using Markdown formatting.

MAMKit

Fri, 01 Mar 2024 00:00:00 +0000

MARGOT

Fri, 01 Mar 2024 00:00:00 +0000

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Sun, 01 Jan 2023 00:00:00 +0000

Add the full text or supplementary notes for the publication here using Markdown formatting.

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Sun, 01 Jan 2023 00:00:00 +0000

Add the full text or supplementary notes for the publication here using Markdown formatting.

Argument Mining In Covid-19 Articles (AMICA)

Fri, 01 Jan 2021 00:00:00 +0000

The objective of the AMICA project was to exploit the argumentative content present in the scientific literature regarding Covid-19 to improve the retrieval of relevant and reliable articles. The project involved both medical and artificial intelligence experts and aimed to develop an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19.

Argument Mining | Language Technologies Lab

Argument Mining on Legal Datasets

Hate Speech Detection with Argumentative Reasoning

Multimodal Argument Mining

AMELR

ArgMining

Argument Mining

Argumentative Fallacies

Multimodality

Dialogues

Interpretability

Reasoning in LLMs

The 13th Workshop on Argument Mining and Reasoning Co-located with ACL 2026

Introduction

Useful Links

Overview of MM-ArgFallacy2025 on Multimodal Argumentative Fallacy Detection and Classification in Political Debates

Overview

Tasks

Data

Test Set Access 🔍

For Fallacy Classification (afc):

For Fallacy Detection (afd):

References

Evaluation

Baseline Results on Test Set

Argumentative Fallacy Classification (AFC) – Macro F1-score

Argumentative Fallacy Detection (AFD) – Binary F1-score

Submission

🏆 Leaderboard – Shared Task Results

AFC Task – Argumentative Fallacy Classification

📝 Text-only

🔊 Audio-only

🔁 Text-Audio

AFD Task – Argumentative Fallacy Detection

📝 Text-only

🔊 Audio-only

🔁 Text-Audio

Key Dates (Anywhere on Earth)

Task Organizers

Eleonora Mancini

Federico Ruggeri

Serena Villata

Paolo Torroni

Contacts

Cite

Credits

MAMKit: A Comprehensive Multimodal Argument Mining Toolkit

MAMKit

MARGOT

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Argument Mining In Covid-19 Articles (AMICA)

For Fallacy Classification (`afc`):

For Fallacy Detection (`afd`):

`AFC Task – Argumentative Fallacy Classification`

`AFD Task – Argumentative Fallacy Detection`