Join Together? Combining Data to Parse Italian Texts %and Addressing the Challenge of Non-Projectivity
Authors: Claudia Corbetta, Giovanni Moretti, Marco Carlo Passarotti (Università di Bergamo, Università di Pavia, Università Cattolica del Sacro Cuore, 2024)
Topic: Syntax and Dependency Treebanks

Abstract
In this paper, we create and evaluate non-combined and combined models using Old and Contemporary Italian data to determine whether increasing the size of the training data with a combined model could improve parsing accuracy to facilitate manual annotation. We find that, despite the increased size of the training data, in-domain parsing performs better. Additionally, we discover that models trained on Old Italian data perform better on Contemporary Italian data than the reverse. We attempt to explain this result in terms of syntactic complexity, finding that Old Italian text exhibits higher sentence length and non-projectivity rate. with \verb|| and ends with \verb||. This is an abstract.

Introduction
High-quality textual data (semi-)manually enhanced with different layers of metalinguistic annotation are extremely valuable resources for conducting linguistic analysis. As for the syntactic layer of annotation, the de facto standard for dependency-based annotation is Universal Dependencies (UD),.} an initiative that provides machine-readable annotations for a wide variety of languages, including historical languages AUTHOR. At the current state of art, Contemporary Italian is well-represented in UD, whereas Old Italian is only represented by one annotated text (a portion of the Divine Comedy of Dante Alighieri). The creation of additional Old Italian annotated data is therefore advisable. %to facilitate cross-Italian studies Since a fully manual annotation process is time-consuming and requires significant effort, we aim to expedite it by using a parser that pre-parses the data, leaving the human annotator with only a manual revision task. To address this, given the scarcity of Old Italian data, we create a combined parser using both Contemporary and Old Italian data. The objective is to determine whether a combined model with an expanded training dataset performs better compared to non-combined models (see AUTHOR for Spanish language and AUTHOR for Stanza combined models). \textcolor{blue}{Introduzione. Cerca letteratura su combined di modelli per lingue storiche. PARLARE DI CREAZIONE DI MODELLI CON UD - INTRODURRE UD E SINTASSI A DIPENDENZE. \textcolor{red}{VA RIFATTA DA ZERO Our aim is twofold: a) to evaluate whether creating a combined model with more linguistic data, even though from different periods, can achieve better scores compared to using a model based on in-domain data. This point could shed light on the possibility of using combined models with a large amount of data for parsing other Italian texts, specifically in Old Italian texts for which gold-annotated data are scarce. b) to understand, through the analysis of recurrent mistakes, possible differences in syntactic patterns. We aim to determine whether these differences are due to: i) a divergence in annotation style, or ii) variations in the diachrony of the language or genre. The paper is organised as follows: Section Sec1 provides a brief description of the Italian language, the syntactic resources and the Italian data available; Section Sec2 details the data used for the experiments, presents the performances of non-combined and combined models, and evaluates their performances; Section Sec3 analyzes the syntactic complexity of each test set (Old and Contemporary Italian) to address accuracy differences; and finally, Section Sec5 provides the conclusion. CEUR-WS's article template provides a consistent \LaTeX{} style for use across CEUR-WS publications, and incorporates accessibility and metadata-extraction functionality. This document will explain the major features of the document class. If you are new to publishing with CEUR-WS, this document is a valuable guide to the process of preparing your work for publication. The ``\verb|ceurart|'' document class can be used to prepare articles for any CEUR-WS publication, and for any stage of publication, from review to final ``camera-ready'' copy with {\itshape very} few changes to the source. This class depends on the following packages for its proper functioning: \item \verb|natbib.sty| for citation processing; \item \verb|geometry.sty| for margin settings; \item \verb|graphicx.sty| for graphics inclusion; \item \verb|hyperref.sty| optional package if hyperlinking is required in the document; \item \verb|fontawesome5.sty| optional package for bells and whistles. All the above packages are part of any standard \LaTeX{} installation. Therefore, the users need not be bothered about downloading any extra packages.

Conclusion
In this paper, we create and evaluate non-combined and combined models of Old Italian and Contemporary Italian data..} In light of the scarcity of manually annotated Old Italian data compared to the richness of Contemporary Italian data, the aim of this work is to determine whether combining data to train a combined model could lead to better accuracy in parsing, thereby facilitating the process for human annotators. We observe that combining Contemporary Italian and Old Italian data, even though it increases the data size of the model, does not lead to better LAS and UAS accuracy scores. This confirms, in line with other studies AUTHOR, that having an in-domain training set is preferable. Additionally, we notice that the model trained on OI data performs better on Contemporary Italian texts than the reverse (i.e. models trained on Contemporary data on OI texts). To explain these results, we investigate the syntactic complexity of each test set (OI, CI-ISDT, and CI-VIT). Specifically we evaluate sentence length, tree depth, lexical density and the type-token ratio. We notice that the tests differ only in the sentence length. We then proceed to calculate another parameter of syntactic complexity, namely non-projectivity. We discover that OI texts present a higher number of non-projective sentences. We hypothesize that the high level of non-projectivity could be connected to the genre of OI text, namely poetry. Thus far, the lack of UD treebanks for OI prose texts and for CI poetry texts have prevented us from investigating whether the high degree of non-projectivity observed in OI test (based on the Italian-Old treebank) is characteristic of the poetry genre or specific to OI. Such question will be left for further studies. Finally, we are currently working to increase the amount of manually annotated OI data, expanding both the range of authors and the genres of the texts considered. This will allow us to evaluate the model's performance both within and outside its domain (in terms of authorship and text typology), as well as to assess its potential applicability to other OI texts..