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Being aware of frames, i.e., the aspect-based grouping of arguments, is crucial in applications that build upon a corpus of arguments, allowing, among others, biases and filter bubbles to be mitigated. However, manually identifying and naming these frames can be time-consuming and therefore not feasible for larger datasets. Within this work, we present a sequential three-step pipeline for automating this task in a data-driven manner. After embedding the arguments, we apply clustering algorithms for identifying the frames and subsequently, utilize methods from the field of cluster labeling to name the frames. The proposed approach is tailored towards the requirements of practical applications where arguments may not be easily split into their argumentative units and hence can belong to more than one frame. Performing a component-wise evaluation, we determine the best-performing configuration of the pipeline. Our results indicate that frames should be identified by performing overlapping and not exclusive clustering and the naming of frames can be accomplished best by extracting aspect terms and weighting them with c-TF-IDF.

ACL 2025

Automatic Identification and Naming of Overlapping and Topic-specific Argumentation Frames

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

A crucial task when analyzing arguments is to determine their quality. Especially when you have to choose from a large number of suitable arguments, the determination of a reliable argument quality value is of great benefit. Probably the best-known model for determining such an argument quality value was developed in IBM's Project Debater and made available to the research community free of charge via an API. In fact, the model was never open and the API is no longer available. In this paper, IBM's model is reproduced using the freely available training data and the description in the corresponding publication. Our reproduction achieves similar results on the test data as described in the original publication. Further, the predicted quality scores of reproduction and original show a very high correlation (Pearson's $r=0.9$) on external data.

Reproducing the Argument Quality Prediction of Project Debater

This paper explores the application of argument mining to mental health narratives using zero‑shot transfer learning. We fine‑tune a BERT‑based sentence classifier on $\sim$15k essays from the Persuade dataset—achieving 69.1\% macro‑F1 on its test set—and apply it without domain adaptation to the CAMS dataset, which consists of anonymized mental health–related Reddit posts. On a manually annotated gold‑standard set of 150 CAMS sentences, our model attains 54.7\% accuracy and 48.9\% macro‑F1, with evidence detection (F1 = 63.4\%) transferring more effectively than claim identification (F1 = 32.0\%). Analysis across expert‑annotated causal factors of distress shows that personal narratives heavily favor experiential evidence (65–77\% of sentences) compared to academic writing. The prevalence of evidence sentences, many of which appear to be grounded in lived experiences, such as descriptions of emotional states or personal events, suggests that personal narratives favor descriptive recollection over formal, argumentative reasoning. These findings underscore the unique challenges of argument mining in affective contexts and offer recommendations for enhancing argument mining tools within clinical and digital mental health support systems.

Reasoning Under Distress: Mining Claims and Evidence in Mental Health Narratives

In the study of argumentation, the schemes introduced by Walton et al. (2008) represent a significant advancement in understanding and analyzing the structure and function of arguments. Walton's framework is particularly valuable for computational reasoning, as it facilitates the identification of argument patterns and the reconstruction of enthymemes. Despite its practical utility, automatically identifying these schemes remains a challenging problem. To aid human annotators, Visser et al. (2021) developed a decision tree for scheme classification. Building on this foundation, we propose a means-end approach to argument scheme classification that systematically leverages expert knowledge---encoded in a decision tree---to guide language models through a complex classification task. We assess the effectiveness of the means-end approach by conducting a comprehensive comparison with a standard multi-class approach across two datasets, applying both prompting and supervised learning methods to each approach. Our results indicate that the means-end approach, when combined with supervised learning, achieves scores only slightly lower than those of the multi-class classification approach. At the same time, the means-end approach enhances explainability by identifying the specific steps in the decision tree that pose the greatest challenges for each scheme---offering valuable insights for refining the overall means-end classification process.

Multi-Class versus Means-End: Assessing Classification Approaches for Argument Patterns

This paper addresses the problem of cross-register generalization in argument mining within political discourse. We examine whether models trained on adversarial, spontaneous U.S. presidential debates can generalize to the more diplomatic and prepared register of UN Security Council (UNSC) speeches. To this end, we conduct a comprehensive evaluation across four core argument mining tasks. Our experiments show that the tasks of detecting and classifying argumentative units transfer well across registers, while identifying and labeling argumentative relations remains notably challenging, likely due to register-specific differences in how argumentative relations are structured and expressed. As part of this work, we introduce ArgUNSC, a new corpus of 144 UNSC speeches manually annotated with claims, premises, and their argumentative links. It provides a resource for future in- and cross-domain studies and novel research directions at the intersection of argument mining and political science.

From Debates to Diplomacy: Argument Mining Across Political Registers

Psychological research has long suggested that storytelling can shape beliefs and behaviors by fostering emotional engagement and narrative transportation. However, it remains unclear whether these effects extend to online argumentative discourse. In this paper, we examine the role of narrative in real-world argumentation using discussions from the ChangeMyView subreddit. Leveraging an automatic story detection model, we analyze how narrative use varies across persuasive comments, user types, discussion outcomes, and the kinds of change being sought. While narrative appears more frequently in some contexts, it is not consistently linked to successful persuasion. Notably, highly persuasive users tend to use narrative less, and storytelling does not demonstrate increased effectiveness for any specific type of persuasive goals. These findings suggest that narrative may play a limited and context-dependent role in online discussions, highlighting the need for computational models of argumentation to account for rhetorical diversity.

Storytelling in Argumentative Discussions: Exploring the Use of Narratives in ChangeMyView

Argument mining is the task of identifying the argument structure of a text: claims, premises, support/attack relations, etc. However, determining the complete argument structure can be quite involved, especially for unpolished texts from online forums, while for many applications the identification of argumentative key statements would suffice (e.g., for argument search). To this end, we introduce and investigate the new task of segmenting an argumentative text by its key statements. We formalize the task, create a first dataset from online communities, propose an evaluation scheme, and conduct a pilot study with several approaches. Interestingly, our experimental results indicate that none of the tested approaches (even LLM-based ones) can actually satisfactorily solve key statement segmentation yet.

Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web

Critical questions (CQs) generation for argumentative texts is a key task to promote critical thinking and counter misinformation. In this paper, we present a two-step approach for CQs generation that i) uses a large language model (LLM) for generating candidate CQs, and ii) leverages a fine-tuned classifier for ranking and selecting the top-k most useful CQs to present to the user. We show that such usefulness-based CQs selection consistently improves the performance over the standard application of LLMs. Our system was designed in the context of a shared task on CQs generation hosted at the 12th Workshop on Argument Mining, and represents a viable approach to encourage future developments on CQs generation.

ARG2ST at CQs-Gen 2025: Critical Questions Generation through LLMs and Usefulness-based Selection

This paper presents a new system for generating critical questions in debates, developed for the Critical Questions Generation shared task. Our two-stage approach, combining generation and classification, utilizes LLaMA 3.1 Instruct models (8B, 70B, 405B) with zero-/few-shot prompting. Evaluations on annotated debate data reveal several key insights: few-shot generation with 405B yielded relatively high-quality questions, achieving a maximum possible punctuation score of 73.5. The 70B model outperformed both smaller and larger variants on the classification part. The classifiers showed a strong bias toward labeling generated questions as Useful, despite limited validation. Further, our system, ranked 6th, outperformed baselines by 3%. These findings stress the effectiveness of large-sized models for question generation and medium-sized models for classification, and suggest the need for clearer task definitions within prompts to improve classification accuracy.

TriLLaMa at CQs-Gen 2025: A Two-Stage LLM-Based System for Critical Question Generation

In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates. Our approach uses pretrained Transformer-based models and proposes several ways to leverage context. In the fallacy classification subtask, our models achieved macro F1-scores of 0.4444 (text), 0.3559 (audio), and 0.4403 (multimodal). Our multimodal model showed performance comparable to the text-only model, suggesting potential for improvements.

Leveraging context for multi-modal fallacy classification in political debates

We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies' progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. 
Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task.

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