Gianluca Moro

Transformer-based language models have sparked a revolutionary change in Legal NLP, endowing lawyers with unparalleled tools to effectively navigate, understand, and draft large volumes of text. However, the dearth of large-scale datasets from authoritative sources hampers further progress. The available resources are primarily single-task, English-only, and written in layman’s terms. To bridge this gap, we introduce Comma , a multi-task and multi-lingual archive of 14K verdicts drawn from the Constitutional Court of the Italian Republic, grounded in a non-common law system. Documents in Comma diverge from ordinary legal manuscripts as they address fundamental principles and rights, involve technical jargon, exhibit an articulated structure, are diachronic, have extended length, and demand more significant expertise and interpretation. By embracing 4 widespread languages, Comma tackles a panoply of necessity-driven tasks: multi-granular abstractive summarization, decision generation, article retrieval, and ruling classification. We systematically benchmark a catalog of language models in both few-shot and full settings, uncovering substantial headroom for improvement. We contribute to the new era of Legal NLP systems by openly releasing Comma and best-performing models (https://github.com/disi-unibo-nlp/comma).

Women are twice as likely as men to face online harassment due to their gender. Despite recent advances in multimodal content moderation, most approaches still overlook the social dynamics behind this phenomenon, where perpetrators reinforce prejudices and group identity within like-minded communities. Graph-based methods offer a promising way to capture such interactions, yet existing solutions remain limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. In this work, we present MemeWeaver, an end-to-end trainable multimodal framework for detecting sexism and misogyny through a novel inter-meme graph reasoning mechanism. We systematically evaluate multiple visual--textual fusion strategies and show that our approach consistently outperforms state-of-the-art baselines on the MAMI and EXIST benchmarks, while achieving faster training convergence. Further analyses reveal that the learned graph structure captures semantically meaningful patterns, offering valuable insights into the relational nature of online hate.

This paper explores advancements in Natural Language Processing (NLP) for legal lay summarization by systematically analyzing existing methodologies, datasets, and research findings. We review current literature, highlighting key challenges such as data scarcity and the complexity of legal language. A primary contribution of this study is the development of LegalEase, a specialized dataset designed to improve model training for summarizing legal documents in layman’s terms. Our findings demonstrate that subdomain-specific datasets within the legal domain outperform general legal datasets in enhancing NLP model performance for generating accurate and comprehensible legal summaries. The insights and methodologies presented provide a foundation for future research in legal lay summarization.

Transformer-based pretrained language models (PLMs) face scalability issues due to their computational expense, which increases with the length of the input sequence, and often struggle to maintain focus on relevant information. To mitigate this, we introduce PrunePert, a novel model featuring a learnable mechanism that identifies and removes uninformative tokens from the context. By doing so, our method not only addresses performance concerns but also enhances interpretability, offering valuable insights into the tokens utilized in the model's decision-making process. Specifically, our approach employs a differentiable perturbed top-$k$ token selection module within the transformer layers to prune a user-defined percentage of tokens. It can be integrated with any downstream PLMs, allowing them to be trained end-to-end using backpropagation. We demonstrate the application of PrunePert in text summarization and classification tasks, utilizing both encoder–decoder PLMs and contemporary decoder-only large language models. Notably, our findings reveal that models equipped with PrunePert achieve up to 2x higher throughput and exhibit comparable performance in text summarization, while demonstrating superior performance in text classification tasks. Code is available at https://github.com/disi-unibo-nlp/prunepert.

Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extreme output dimensions. These challenges are exemplified in the European Food Safety Authority’s FoodEx2 system–a standardized food classification framework essential for food consumption monitoring and contaminant exposure assessment across Europe. FoodEx2 coding transforms natural language food descriptions into a set of codes from multiple standardized hierarchies, but faces implementation barriers due to its complex structure. Given a food description (e.g., “organic yogurt”), the system identifies its base term (“yogurt”), all the applicable facet categories (e.g., “production method”), and then, every relevant facet descriptors to each category (e.g., “organic production”). While existing models perform adequately on well-balanced and semantically dense hierarchies, no work has been applied on the practical constraints imposed by the FoodEx2 system. The limited literature addressing such real-world scenarios further compounds these challenges. We propose FEAST (Food Embedding And Semantic Taxonomy), a novel retrieval-augmented framework that decomposes FoodEx2 classification into a three-stage approach: (1) base term identification, (2) multi-label facet prediction, and (3) facet descriptor assignment. By leveraging the system’s hierarchical structure to guide training and performing deep metric learning, FEAST learns discriminative embeddings that mitigate data sparsity and improve generalization on rare and fine-grained labels. Evaluated on the multilingual FoodEx2 benchmark, FEAST outperforms the prior European’s CNN baseline F1 scores by 12–38% on rare classes.