Arda Yüksel, Gabriel Thiem, Susanne Walter, Patrick Felka, Gabriela Alves Werb, Ivan Habernal. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Sebastian Ochs, Ivan Habernal. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the key parameter, privacy budget \varepsilon that governs the strength of privacy protection, remains largely arbitrary. We argue that determining the \varepsilon value should not be solely in the hands of researchers or system developers, but must also take into account the actual people who share their potentially sensitive data. In other words: Would you share your instant messages for \varepsilon of 10? We address this research gap by designing, implementing, and conducting a behavioral experiment (311 lay participants) to study the behavior of people in uncertain decision-making situations with respect to privacy-threatening situations. Framing the risk perception in terms of two realistic NLP scenarios and using a vignette behavioral study help us determine what \varepsilon thresholds would lead lay people to be willing to share sensitive textual data – to our knowledge, the first study of its kind.

What is preventing us from building a NLP system that could help real people in real situations, for instance when they need legal advice but don't understand law?This question is trickier than one might think, because legal systems vary from country to country, so do the law books, availability of data, and incomprehensibility of legalese.In this paper we focus Germany (which employs the civil-law system where, roughly speaking, interpretation of law codes dominates over precedence) and lay a foundational work to address the laymen's legal question answering empirically.We create GerLayQA, a new dataset comprising of 21k laymen's legal questions paired with answers from lawyers and grounded to concrete law book paragraphs.We experiment with a variety of retrieval and answer generation models and provide an in-depth analysis of limitations, which helps us to provide first empirical answers to the question above.

Deep learning models for NLP tasks are prone to variants of privacy attacks.To prevent privacy leakage, researchers have investigated word-level perturbations, relying on the formal guarantees of differential privacy (DP) in the embedding space.However, many existing approaches either achieve unsatisfactory performance in the high privacy regime when using the Laplacian or Gaussian mechanism, or resort to weaker relaxations of DP that are inferior to the canonical DP in terms of privacy strength.This raises the question of whether a new method for private word embedding can be designed to overcome these limitations.In this paper, we propose a novel private embedding method called the high dimensional truncated Laplacian mechanism.Specifically, we introduce a non-trivial extension of the truncated Laplacian mechanism, which was previously only investigated in one-dimensional space cases.Theoretically, we show that our method has a lower variance compared to the previous private word embedding methods.To further validate its effectiveness, we conduct comprehensive experiments on private embedding and downstream tasks using three datasets.Remarkably, even in the high privacy regime, our approach only incurs a slight decrease in utility compared to the non-private scenario.

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent.

Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price.We know that stricter privacy guarantees in differentiallyprivate stochastic gradient descent (DP-SGD) generally degrade model performance.However, previous research on the efficiency of DP-SGD in NLP is inconclusive or even counter-intuitive.In this short paper, we provide an extensive analysis of different privacy preserving strategies on seven downstream datasets in five different 'typical' NLP tasks with varying complexity using modern neural models based on BERT and XtremeDistil architectures.We show that unlike standard non-private approaches to solving NLP tasks, where bigger is usually better, privacypreserving strategies do not exhibit a winning pattern, and each task and privacy regime requires a special treatment to achieve adequate performance.

Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as documents with people’s profiles or relationships as edges, are prone to privacy leaks, as the trained model might reveal the original input. Although differential privacy (DP) offers a well-founded privacy-preserving framework, GCNs pose theoretical and practical challenges due to their training specifics. We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also provides strong privacy guarantees of epsilon = 1.0. We show that, under certain modeling choices, privacy-preserving GCNs perform up to 90% of their non-private variants, while formally guaranteeing strong privacy measures.

Differential privacy provides a formal approach to privacy of individuals.Applications of differential privacy in various scenarios, such as protecting users' original utterances, must satisfy certain mathematical properties.Our contribution is a formal analysis of ADePT, a differentially private autoencoder for text rewriting (Krishna et al., 2021).ADePT achieves promising results on downstream tasks while providing tight privacy guarantees.Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated.We also quantify the impact of the error in its private mechanism, showing that the true sensitivity is higher by at least factor 6 in an optimistic case of a very small encoder's dimension and that the amount of utterances that are not privatized could easily reach 100% of the entire dataset.Our intention is neither to criticize the authors, nor the peer-reviewing process, but rather point out that if differential privacy applications in NLP rely on formal guarantees, these should be outlined in full and put under detailed scrutiny. Theoretical backgroundFrom a high-level perspective, DP works with the notion of individuals whose information is contained in a database (dataset).Each individual's datapoint (or record), which could be a single bit, a number, a vector, a structured record, a text document, or any arbitrary object, is considered private

Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typology of ad hominem arguments as well as their potential causes, this paper fills this gap by (1) performing several large-scale annotation studies, (2) experimenting with various neural architectures and validating our working hypotheses, such as controversy or reasonableness, and (3) providing linguistic insights into triggers of ad hominem using explainable neural network architectures.

Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Argument mining has become a popular research area in NLP.It typically includes the identification of argumentative components, e.g.claims, as the central component of an argument.We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently.To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art featurerich and deep learning systems, to identify claims in a cross-domain fashion.While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.

We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness.We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged.We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data.We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness.We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78accuracy and 0.35-0.40Spearman's correlation in a cross-topic evaluation.We release the newly created corpus UKPConvArg1 and the experimental software under open licenses.

This article tackles a new challenging task in computational argumentation.Given a pair of two arguments to a certain controversial topic, we aim to directly assess qualitative properties of the arguments in order to explain why one argument is more convincing than the other one.We approach this task in a fully empirical manner by annotating 26k explanations written in natural language.These explanations describe convincingness of arguments in the given argument pair, such as their strengths or flaws.We create a new crowd-sourced corpus containing 9,111 argument pairs, multilabeled with 17 classes, which was cleaned and curated by employing several strict quality measures.We propose two tasks on this data set, namely (1) predicting the full label distribution and (2) classifying types of flaws in less convincing arguments.Our experiments with feature-rich SVM learners and Bidirectional LSTM neural networks with convolution and attention mechanism reveal that such a novel fine-grained analysis of Web argument convincingness is a very challenging task.We release the new corpus UKPConvArg2 and the accompanying software under permissive licenses to the research community.

Focused retrieval (a.k.a., passage retrieval) is important at its own right and as an intermediate step in question answering systems. We present a new Web-based collection for focused retrieval. The document corpus is the Category A of the ClueWeb12 collection. Forty-nine queries from the educational domain were created. The $100$ documents most highly ranked for each query by a highly effective learning-to-rank method were judged for relevance using crowdsourcing. All sentences in the relevant documents were judged for relevance.

Analyzing arguments in user-generated Web discourse has recently gained atten-tion in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learn-ing, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error-prone, and highly context-dependent. We asked whether leveraging unlabeled data in a semi-supervised manner can boost the performance of argument component identification and to which extent is the approach independent of domain and reg-ister. We propose novel features that ex-ploit clustering of unlabeled data from de-bate portals based on a word embeddings representation. Using these features, we significantly outperform several baselines in the cross-validation, cross-domain, and cross-register evaluation scenarios. 1