Lea Kawaletz


2024

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Using Discourse Connectives to Test Genre Bias in Masked Language Models
Heidrun Dorgeloh | Lea Kawaletz | Simon Stein | Regina Stodden | Stefan Conrad
Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)

This paper presents evidence for an effect of genre on the use of discourse connectives in argumentation. Drawing from discourse processing research on reasoning based structures, we use fill-mask computation to measure genre-induced expectations of argument realisation, and beta regression to model the probabilities of these realisations against a set of predictors. Contrasting fill-mask probabilities for the presence or absence of a discourse connective in baseline and finetuned language models reveals that genre introduces biases for the realisation of argument structure. These outcomes suggest that cross-domain discourse processing, but also argument mining, should take into account generalisations about specific features, such as connectives, and their probability related to the genre context.

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The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
Nailia Mirzakhmedova | Johannes Kiesel | Milad Alshomary | Maximilian Heinrich | Nicolas Handke | Xiaoni Cai | Valentin Barriere | Doratossadat Dastgheib | Omid Ghahroodi | MohammadAli SadraeiJavaheri | Ehsaneddin Asgari | Lea Kawaletz | Henning Wachsmuth | Benno Stein
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touché23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset’s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touché23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.

2023

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Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse
Regina Stodden | Laura Kallmeyer | Lea Kawaletz | Heidrun Dorgeloh
Proceedings of the 10th Workshop on Argument Mining

This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance. Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it. The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although. By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse. While the proposed system does not aim to outperform state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.

2022

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Developing an argument annotation scheme based on a semantic classification of arguments
Lea Kawaletz | Heidrun Dorgeloh | Stefan Conrad | Zeljko Bekcic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper introduces a semantic classification of arguments that can help to facilitate argument detection. We report on our experiences with corpus annotations using a function-based classification of arguments and a procedure for operationalizing the scheme by using semantic templates.