Language Varieties of I taly: Technology Challenges and Opportunities (2024)

Abstract: Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions, and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within a few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy’s language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.

## introduction the natural language processing (nlp) community has recently started to include endangered languages in its repertoire, and language varieties of italy are no exception. however, most of the efforts in nlp implicitly assume that these language varieties are just under-resourced entities (in terms of written data availability) with an established written form, and with the same functions and technological needs of high-resource standardized languages with institutional support, such as italian or english @xcite . this machine-centric approach not only fails to acknowledge that most endangered languages are primarily oral, without a standardized orthography and canonical variant, often code-switched with a co-territorial ''high-prestige'' standardized language, and serving different language functions to other languages within the local linguistic ecosystem @xcite , but also disregards what and how technologies should be built to safeguard endangered languages in the interest of speech communities @xcite . in this paper, we discuss the technology challenges and opportunities for language varieties of italy, one of the most linguistically diverse landscapes in europe which, according to unesco @xcite , currently counts over 30 languages in danger. italy's languages and dialects are not only many for such a small area @xcite ), but they are also very different from each other, and their linguistic distance does not typically relate to geographical distance @xcite . most of these varieties are romance, albeit germanic, slavic, albanian, and hellenic ones also shape the italian linguistic landscape. as for the majority of endangered languages, most of italy's language varieties comprise many local variants, have no standardized written form, and are just occasionally written, insofar as they are primarily used in spoken, informal settings. they typically exist in a peculiar diglossic situation with italian, and vary in terms of recognition, protection, economic incentives, and prospects. after introducing the linguistic situation in italy (section 2), we review efforts in nlp for its languages and dialects (section 3). we then discuss the machine-centric assumptions of the default nlp approach when dealing with these varieties, namely, the exaggerated focus on ''machinereadable'' written data, the little regard for the representativeness of such materials of speech communities, and the homogeneous view of functions, uses, and needs across language varieties (section 4). we argue that language varieties of italy should not be approached as a data commodity for machine learning advances, and that technology should serve language varieties and their speakers and not the other way round. we thus present recommendations and opportunities for speaker-centric nlp and advocate for a local community aimed at responsibly supporting vitality of italy's varieties through sensitization on ethical engagement, sharing of practices, participatory collaboration, and active awareness-raising (section 5). finally, we provide our conclusions (section 6). ## language varieties of italy and nlp the study, preservation, and promotion of language diversity have recently gained increasing attention in the nlp community. initiatives such as the acl 2022 special theme on ''language diversity: from low-resource to endangered languages'' @xcite , the acl special interest group sigel, foot_3 and relevant workshops, e.g., computel @xcite , eurali @xcite , americasnlp @xcite , have been proposed. moreover, the var-dial series of workshops @xcite is being routinely organized to promote the study of diatopic variation of language varieties and dialects. in the following, we review previous work in nlp for italy's varieties, from monolingual (section 3.1) to multilingual efforts (section 3.2), and highlight commonalities and differences in the shortcomings of both research lines (section 3.3). ## the default machine-centric approach in this section, we provide an in-depth overview of the main assumptions and shortcomings of the default nlp approach-what we refer to as machine-centric nlp-with a focus on language varieties of italy. we first discuss the persistent focus on written data scarcity and how this is often perceived as a problem to be solved (section 4.1). second, we focus on widespread text collections that are typically used for training language models, arguing that the common practice of language data as a commodity fails to represent language varieties and their speakers (section 4.2). lastly, we discuss the intrinsic assumptions of the standard approach, namely the lack of regard for functions, contexts and needs of varieties (section 4.3). ## towards a speaker-centric approach the assumptions and shortcomings discussed in the preceding sections make evident that the current machine-centric approach neither respects nor represents language varieties of italy and their speakers. ultimately, language technology should serve speech communities and their language varieties, and not the other way round. we need to identify new ways of working that are centered on speech communities and their varieties-what we refer to as speaker-centric approach. in this section, we provide recommendations and opportunities towards speaker-centric work that foresees active engagement with speech communities. ## conclusion in this work, we present the complex linguistic landscape of italy, shedding light on the main assumptions and shortcomings of the default, machine-centric nlp approach for local language varieties. we advocate for a shift in the paradigm towards speaker-centric nlp, and provide recommendations and opportunities for responsible, participatory work aimed to support vitality of language varieties of italy, designed with speech communities, for serving speakers and their needs.