W eb NLG - IT : Construction of an aligned RDF - I talian corpus through Machine Translation techniques (2025)

Abstract: The main goal of this work is the creation of the Italian version of the WebNLG corpus through the application of Neural Machine Translation (NMT) and post-editing with hand-written rules. To achieve this goal, in a first step, several existing NMT models were analysed and compared in order to identify the system with the highest performance on the original corpus. In a second step, after using the best NMT system, we semi-automatically designed and applied a number of rules to refine and improve the quality of the produced resource, creating a new corpus named WebNLG-IT. We used this resource for fine-tuning several LLMs for RDF-to-text tasks. In this way, comparing the performance of LLM-based generators on both Italian and English, we have (1) evaluated the quality of WebNLG-IT with respect to the original English version, (2) released the first fine-tuned LLM-based system for generating Italian from semantic web triples and (3) introduced an Italian version of a modular generation pipeline for RDF-to-text.

## introduction webnlg was originally created in 2017 in the context of the first webnlg challenge @xcite for generating english texts starting from an rdf set of triples, but it has been used as a reference corpus in several shared task competitions foot_0 . as a consequence of this quite large use, several works converted webnlg into different languages, that are primarily german @xcite and russian @xcite , and partially maltese, irish, breton and welsh @xcite . the present work aims to create the first aligned italian rdf-to-text corpus, with the objective of enriching italian resources in the domain of natural language generation (nlg). in order to obtain a high-quality complete italian version of webnlg 3.0, we follow the approach of @xcite : in a first step, we apply a high-quality neural machine translation system on the original english webnlg 3.0, and in a second step, we improve the quality of the translations by using hand-written rules. the first step of corpus construction was conducted using the deepl machine translation system, after a preliminary phase of analysis and comparison with other neural machine translation models. 2 this comparison relied on specific metrics for evaluating machine translations. additionally, a human evaluation of the deepl outputs was conducted to assess the adequacy and fluency of the translations. in the second step, after the automatic translation, a post-editing phase was carried out to improve the quality of the resource, following a similar approach to @xcite , thus generating the first version of the italian corpus, called webnlg-it. 3 during this phase, a classification of the detected errors was conducted following the taxonomy provided by @xcite , in order to further analyze the corpus, resulting in an additional dataset of 198 labeled errors. in contrast to @xcite , our work introduces a novel step, in which italian and multilingual llms were fine-tuned for the rdf-to-text task. finally, to further enhance the performance of llm-based rdf-to-text systems, we integrated the fine-tuned models into a modular generation pipeline for the italian text generation. in summary, the main contributions we present in this work are: ## from webnlg to webnlg-it webnlg is a linguistic resource composed of data units, each accompanied by one or more verbalizations manually written by expert annotators. the generated verbalization aims to capture the semantics represented by the given starting unit. for example: ## conclusion the main objective of this work was the creation of the webnlg-it corpus. to achieve this goal, we used machine translation techniques to translate the corpus from english to italian, followed by post-editing to enhance the quality of the produced resource. for the machine translation, we chose the deepl model after a comparison through automatic metrics with other translation models, followed by an automatic evaluation phase to assess the output produced by the chosen model. the post-editing process involved a semi-automatic approach, consisting of the automatic extraction of errors and their manual correction. to demonstrate the quality of the produced resource, we tested various large language models, showing that our resource enables the models to achieve good performance in rdf-to-text generation. finally, we implemented a natural language generation pipeline that integrates llms, but it did not achieve the desired results. future work on this generation pipeline could involve the use of fine-tuning for the sentence aggregation step.