We are very grateful to the reviewers for their comments, most of which we have implemented. We were not able to fully address the following points: 1. "Preliminary comparison metrics (e.g. parsing accuracy gain) between the old TTL and the new RODNA syntactic annotations on overlapping data": TTL did not provide syntactic annotations. Preliminary experiments with RODNA are described in the paper, where the tool is compared to STANZA and Trankit. 2. "For ADAMo, what specific features distinguish Moldovan from standard Romanian in the added texts, and how will variety differences be quantified": these experiments are still at a preliminary stage. 3. "Providing example queries/snippets illustrating syntactic annotations": since this annotation layer is not yet indexed in CoRoLa, we cannot provide examples from CoRoLa. However, the syntactic annotation available for German in KorAP (korap.ids-mannheim.de) offers an indication of how such annotations could be queried and displayed in the future.

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.