Enriching the NA rabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language (2023)

Abstract: In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank (Seddah et al., 2020) with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available. Our enhanced NArabizi Treebank paves the way for creating sophisticated language models and NLP tools for this under-represented language.

## introduction despite the abundance of rich and diverse dialects worldwide, each possessing distinctive features and characteristics, many of these dialects still lack the necessary resources and support to enable their speakers to access modern technologies in their own language @xcite . therefore, it is imperative to undertake endeavors aimed at creating annotated corpora, developing language models, and establishing dictionaries and grammars for low-resource dialects. these efforts are crucial for the preservation and advancement of these dynamic languages, which encapsulate unique cultures, histories, and experiences within their respective communities. one notable example of such an effort is the masakhane community, which is dedicated to enhancing natural language processing (nlp) research for african languages through significant initiatives such as masakhaner @xcite . similar efforts are ongoing for indonesian languages @xcite . in addition, a long-standing and somewhat unrelated initiative known as the universal dependencies project @xcite originally aimed to provide a standardized set of syntactic guidelines for a limited number of languages turned out to become the recipient of numerous treebank initiatives for low-resource languages. these initiatives not only adopted the initial guidelines but also expanded upon them to accommodate the unique idiosyncrasies of each language. in this work, we aim to enhance a pre-existing multi-view treebank devoted to a very low-resource language, namely the north-african arabic dialect written in latin script, collected from algerian sources and denoted as the narabizi treebank, the first available for this dialect, where arabizi refers to both the practice of writing arabic using the latin alphabet and n for the north african dialect @xcite . made of noisy user-generated content that exhibits a high level of language variability, its annotations faced many challenges as described by the authors and contained remaining errors @xcite . our work builds on previous efforts to annotate and standardize treebank annotations for lowresource languages to enhance the quality and consistency of linguistic resources @xcite @xcite . following previous research, we consider the impact of refining annotation schemes on downstream tasks. @xcite examine how much a treebank's performance relies on its annotation scheme and whether employing a more linguistically rich scheme would decrease performance. their findings indicate that using a fine-grained annotation for training a parser does not necessarily improve performance when parsing with a coarse-grained tagset. this observation is relevant to our study as we expect refining the treebank could enhance the parsing performance even though the inherent variability of this language, which, tied to its small size treebank, could bring a negative impact on such enhancements. on the other hand, the experiments conducted by @xcite demonstrate that using a cleaner and more coherent treebank yields superior results compared to a treebank with a training set five times larger. this observation highlights the significance of high-quality dataset annotations, particularly for smaller datasets. this understanding primarily drives the goal of improving the narabizi treebank's annotations. in this context, we propose a heavily revised version of narabizi treebank @xcite ) that includes two novel annotation layers for named entity recognition (ner) and offensive language detection. one of the goals of this work is also to study the impact of non-gold tokenization on ner, a scenario almost never investigated by the community @xcite . our primary contributions are as follows: treebanking for user-generated content treebanks and annotated corpora have greatly impacted nlp tools, applications, and research in general. despite the challenges of constructing large and structurally consistent corpora, which requires considerable effort and time, many in the field considered this pursuit valuable and necessary (de @xcite . however, constructing treebanks for user-generated content is more challenging due to the extensive variation in language usage and style, the prevalence of non-standard spellings and grammar, and the necessity for domain-specific annotations @xcite . interest in treebanking user-generated content, such as social media posts and online forum discussions, has risen, and numerous efforts have been undertaken to create treebanks for user-generated content @xcite @xcite @xcite . ## extending a low-resource language treebank in this section, we outline our methodology for expanding and enhancing the narabizi treebank. we start by re-annotating tokenization, morphosyntactic, and syntactic layers to ensure consistency, followed by detailing the annotation guidelines and procedures for ner and offensive language detection. we refer to the initial treebank introduced by @xcite as narabiziv1 and our extended version as narabiziv2. ## dataset evaluation we evaluate the narabiziv2 dataset on ud parsing tasks and ner using standard transfer learning architectures on which we vary the pre-trained language model and the tokenization scenario. ## conclusion in this paper, we present a comprehensive study on the development and refinement of the narabizi treebank @xcite by improving its annotations, consistency, and tokenization, as well as providing new annotations for ner and offensive language. our work contributes to the enhancement of the narabizi treebank, making it a valuable resource for research on low-resource languages and user-generated content with high variability. we explore the impact of tokenization on the refined narabizi treebank, employing the udpipe tokenizer for our evaluation. the results demonstrate the tokenizer's effectiveness in handling various tokens and multiword expressions. our experiments show that training and testing on the narabiziv2 improve the ud tasks performances. furthermore, we show the impact of the tokenization for ner and ud tasks, and we report results using predicted tokenization for evaluation to estimate the models' performance on raw data. future research could emphasize expanding the narabizi treebank towards other dialects and ex-amining the treebank's potential applications in various nlp tasks. our dataset is made freely available as part of the new version of the narabizi treebank foot_6 . the next release will additionally contain a set of other sentence translations prepared by a tunisian speaker. these translations will be interesting for cross-dialect studies, given that the narabizi corpus is predominantly made of algerian dialect.