Yuanhao Ding, Meimingwei Li, Esteban Garces Arias, Matthias Aßenmacher, Christian Heumann, Chongsheng Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Coverage optimization in Wireless Sensor Networks is a fundamental yet NP-hard problem that directly affects monitoring quality and efficiency. Existing solutions mainly rely on meta-heuristic algorithms that use fitness-based evaluations, which often incur high computational overhead, slow convergence, and limited scalability, particularly in real-time or high-precision monitoring scenarios. In this paper, we examine the relationship between effective coverage area and redundant distances in an analytical manner. We then propose reformulating WSN coverage optimization as a Distance-Gradient based convex optimization problem, which can be subsequently solved using the first-order Gradient Descent algorithm or the second-order quasi-Newton algorithm. Extensive comparative experiments against five representative meta-heuristic methods, the Virtual Force Algorithm (VFA) and a general convex optimization algorithm (CVX), demonstrate that our approach achieves near-optimal coverage while preserving network connectivity within milliseconds, highlighting its advantages over existing methods for WSNs coverage optimization.

Decoding strategies for generative large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Guided by specific hyperparameters, these strategies aim to transform the raw probability distributions produced by language models into coherent, fluent text. In this study, we undertake a large-scale empirical assessment of a range of decoding methods, open-source LLMs, textual domains, and evaluation protocols to determine how hyperparameter choices shape the outputs. Our experiments include both factual (e.g., news) and creative (e.g., fiction) domains, and incorporate a broad suite of automatic evaluation metrics alongside human judgments. Through extensive sensitivity analyses, we distill practical recommendations for selecting and tuning hyperparameters, noting that optimal configurations vary across models and tasks. By synthesizing these insights, this study provides actionable guidance for refining decoding strategies, enabling researchers and practitioners to achieve higher-quality, more reliable, and context-appropriate text generation outcomes.

Abstract Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either as derivatives of the prediction function or forward differences in prediction due to changes in feature values. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a general model-agnostic interpretation method for machine learning models. This may stem from the ambiguity surrounding marginal effects and their inability to deal with the non-linearities found in black box models. We introduce a unified definition of forward marginal effects (FMEs) that includes univariate and multivariate, as well as continuous, categorical, and mixed-type features. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for FMEs. Furthermore, we argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to average homogeneous FMEs within population subgroups, which serve as conditional feature effect estimates.

While existing neural network-based approaches have shown promising results in Handwritten Text Recognition (HTR) for highresource languages and standardized/machinewritten text, their application to low-resource languages often presents challenges, resulting in reduced effectiveness.In this paper, we propose an innovative HTR approach that leverages the Transformer architecture for recognizing handwritten Old Occitan language.Given the limited availability of data, which comprises only word pairs of graphical variants and lemmas, we develop and rely on elaborate data augmentation techniques for both text and image data.Our model combines a custom-trained Swin image encoder with a BERT text decoder, which we pre-train using a large-scale augmented synthetic data set and fine-tune on the small human-labeled data set.Experimental results reveal that our approach surpasses the performance of current state-ofthe-art models for Old Occitan HTR, including open-source Transformer-based models such as a fine-tuned TrOCR and commercial applications like Google Cloud Vision.To nurture further research and development, we make our models, data sets, and code publicly available: https://huggingface.co/misoda

Abstract For the latter part of the past decade, Aspect-Based Sentiment Analysis has been a field of great interest within Natural Language Processing. Supported by the Semantic Evaluation Conferences in 2014–2016, a variety of methods has been developed competing in improving performances on benchmark data sets. Exploiting the transformer architecture behind BERT, results improved rapidly and efforts in this direction still continue today. Our contribution to this body of research is a holistic comparison of six different architectures which achieved (near) state-of-the-art results at some point in time. We utilize a broad spectrum of five publicly available benchmark data sets and introduce a fixed setting with respect to the pre-processing, the train/validation splits, the performance measures and the quantification of uncertainty. Overall, our findings are two-fold: First, we find that the results reported in the scientific articles are hardly reproducible, since in our experiments the observed performance most of the time fell short of the reported one. Second, the results are burdened with notable uncertainty, depending on the data splits, which is why a reporting of uncertainty measures is crucial.