Comprehensive Benchmark for Fallacy Detection and Classification: MAFALDA

Core Concepts

MAFALDA is a comprehensive benchmark for fallacy detection and classification that unifies previous fallacy datasets, provides a new taxonomy of fallacies, and introduces a disjunctive annotation scheme to account for the inherent subjectivity in fallacy annotation.

Abstract

The paper introduces MAFALDA, a benchmark for fallacy classification that merges and unites previous fallacy datasets. It proposes a taxonomy that aligns, refines, and unifies existing classifications of fallacies. The paper also provides a manual annotation of a part of the dataset along with manual explanations for each annotation. The key highlights are: MAFALDA unifies previous fallacy datasets into a comprehensive benchmark, covering a diverse range of texts from online discussions, news articles, and political debates. The paper introduces a new taxonomy of fallacies that consolidates and organizes the various fallacy types used in prior work. The taxonomy has three levels of granularity, from broad categories to specific fallacy types. The authors propose a new "disjunctive annotation scheme" that accounts for the inherent subjectivity in fallacy annotation by allowing multiple valid labels for the same textual span. The dataset includes 200 manually annotated texts with 260 instances of fallacies, each with a detailed explanation. The paper evaluates the performance of state-of-the-art language models and humans on the MAFALDA benchmark, demonstrating that the task is challenging and that humans still outperform the models. The analysis reveals that the most challenging fallacies are those related to appeals to emotion, as they often appear in texts without necessarily constituting a fallacy. Overall, MAFALDA provides a comprehensive and standardized benchmark for fallacy detection and classification, addressing the fragmentation and subjectivity issues in prior work.

Stats

"Fallacies can be found in various forms of communication, including speeches, advertisements, Twitter/X posts, and political debates." "Fallacies played a role in the 2016 Brexit referendum and the debate about COVID-19 vaccinations, where fake news spread on news outlets and in social networks." "The dataset contains 9,745 texts, of which 200 texts have been annotated manually, with a total of 268 spans." "The three most frequent fallacies represent 1/4 of the dataset, while the least frequent fallacies appear less than three times."

Quotes

"A fallacy is an erroneous or invalid way of reasoning." "Annotating fallacies is an inherently subjective endeavor." "There are cases where multiple, equally valid annotations can coexist for the same textual span."

Key Insights Distilled From

MAFALDA

by Chad... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2311.09761.pdf

Deeper Inquiries

How can the disjunctive annotation scheme be applied to other subjective NLP tasks beyond fallacy detection?

The disjunctive annotation scheme can be applied to other subjective NLP tasks by allowing for multiple valid annotations for the same span of text. This approach acknowledges the inherent subjectivity in tasks like sentiment analysis, opinion mining, or argumentation mining, where different annotators may interpret the same text differently. By permitting alternative labels for a given span, the scheme accommodates diverse perspectives and interpretations, leading to more comprehensive and nuanced annotations. This can enhance the robustness and flexibility of NLP models trained on such data, enabling them to capture the complexity and variability of human language use in subjective contexts.

What are the potential biases and limitations in the manual annotation process, and how can they be further mitigated?

In the manual annotation process, potential biases may arise from annotators' individual backgrounds, experiences, or interpretations of the task. These biases can impact the consistency and accuracy of annotations, leading to discrepancies in labeling. To mitigate these biases, annotators can undergo training to ensure a shared understanding of the annotation guidelines and criteria. Regular calibration sessions and inter-annotator agreement checks can help maintain consistency among annotators. Additionally, incorporating diverse perspectives in the annotation team, providing clear instructions, and resolving disagreements through discussion or voting can help reduce biases and improve the quality of annotations.

How can the MAFALDA benchmark be extended to support multilingual fallacy detection and classification?

To extend the MAFALDA benchmark for multilingual fallacy detection and classification, several steps can be taken: Multilingual Dataset Collection: Gather fallacy datasets in different languages to create a diverse multilingual corpus for annotation and evaluation. Translation and Annotation: Translate the existing MAFALDA dataset into multiple languages and manually annotate the texts for fallacies in each language. Cross-Lingual Evaluation: Evaluate the performance of language models on multilingual fallacy detection tasks using the annotated datasets. This will help assess the models' ability to generalize across languages. Language-Specific Taxonomies: Develop language-specific taxonomies of fallacies to account for cultural and linguistic variations in how fallacies are expressed in different languages. Fine-Tuning and Transfer Learning: Fine-tune language models on the multilingual fallacy detection data to improve their performance on detecting fallacies in various languages. Transfer learning techniques can also be employed to leverage knowledge from one language to another. By following these steps, the MAFALDA benchmark can be extended to support multilingual fallacy detection and classification, enabling the development of robust NLP models for detecting fallacies in diverse linguistic contexts.