Enhanced Universal Dependencies (EUD) provide a more informative syntactic representation than Basic Universal Dependencies by relaxing tree constraints to allow for graph structures. While conversion rules from basic to enhanced relations have been established for Portuguese, they were previously evaluated only on journalistic text using gold-standard basic syntactic trees. This paper evaluates the robustness of these rules in diverse scenarios ("in the wild"), encompassing other text genres and domains, as well as realistic parsing pipelines that rely on automatically generated basic syntax. Our results demonstrate that Portuguese-specific rules consistently outperform universal rules. However, the reliance on automatic basic syntax significantly impacts performance. This degradation is particularly severe when the domain of the input text differs from the training data of the basic parser. We also provide a detailed error analysis, identifying specific difficult linguistic phenomena and scenarios.

Enhanced Universal Dependencies (EUD) serve as a crucial link between syntax and semantics. Beyond basic syntactic dependencies, EUD provides valuable refined logical connections for downstream tasks such as semantic role labeling, coreference resolution, information extraction, and question answering. The original EUD framework defines six types of relationships, but this paper introduces an extension designed to address subject propagation in pro-drop languages. This “Extended EUD” proposal increases the number of relationships that may be annotated in sentences, improving linguistic representation. Additionally, we report our experiments on a corpus of Portuguese (a pro-drop language), which we make publicly available to the research community.

Sentence simplification (SS) focuses on adapting sentences to enhance their readability and accessibility. While large language models (LLMs) match task-specific baselines in English SS, their performance in Portuguese remains underexplored. This paper presents a comprehensive performance comparison of 26 state-of-the-art LLMs in Portuguese SS, alongside two simplification models trained explicitly for this task and language. They are evaluated under a one-shot setting across scientific, news, and government datasets. We benchmark the models with our newly introduced Gov-Lang-BR corpus (1,703 complex-simple sentence pairs from Brazilian government agencies) and two established datasets: PorSimplesSent and Museum-PT. Our investigation takes advantage of both automatic metrics and large-scale linguistic analysis to examine the transformations achieved by the LLMs. Furthermore, a qualitative assessment of selected generated outputs provides deeper insights into simplification quality. Our findings reveal that while open-source LLMs have achieved impressive results, closed-source LLMs continue to outperform them in Portuguese SS.