TA r C : Incrementally and Semi-Automatically Collecting a T unisian A rabish Corpus (2020)

Abstract: This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC). Arabish, also known as Arabizi, is a spontaneous coding of Arabic dialects in Latin characters and “arithmographs” (numbers used as letters). This code-system was developed by Arabic-speaking users of social media in order to facilitate the writing in the Computer-Mediated Communication (CMC) and text messaging informal frameworks. Arabish differs for each Arabic dialect and each Arabish code-system is under-resourced, in the same way as most of the Arabic dialects. In the last few years, the attention of NLP studies on Arabic dialects has considerably increased. Taking this into consideration, TArC will be a useful support for different types of analyses, computational and linguistic, as well as for NLP tools training. In this article we will describe preliminary work on the TArC semi-automatic construction process and some of the first analyses we developed on TArC. In addition, in order to provide a complete overview of the challenges faced during the building process, we will present the main Tunisian dialect characteristics and its encoding in Tunisian Arabish.

## introduction arabish is the romanization of arabic dialects (ads) used for informal messaging, especially in social networks. 1 this writing system provides an interesting ground for linguistic research, computational as well as sociolinguistic, mainly due to the fact that it is a spontaneous representation of the ads, and because it is a linguistic phenomenon in constant expansion on the web. despite such potential, little research has been dedicated to tunisian arabish (ta). in this paper we describe the work we carried to develop a flexible and multi-purpose ta resource. this will include a ta corpus, together with some tools that could be useful for analyzing the corpus and for its extension with new data. first of all, the resource will be useful to give an overview of the ta. at the same time, it will be a reliable representation of the tunisian dialect (tun) evolution over the last ten years: the collected texts date from 2009 to present. this selection was done with the purpose to observe to what extent the ta orthographic system has evolved toward a writing convention. therefore, the tarc will be suitable for phonological, morphological, syntactic and semantic studies, both in the linguistic and the natural language processing (nlp) domains. for these reasons, we decided to build a corpus which could highlight the structural characteristics of ta through different annotation levels, including part of speech (pos) tags and lemmatization. in particular, to facilitate the match with the already existing tools and studies for the arabic language processing, we provide a transcription in arabic characters at token level, following the conventional orthography for dialectal arabic guidelines coda* (coda star) @xcite and taking into account the specific guidelines for tun (coda tun) @xcite . furthermore, even if the translation is not the main goal of this research, we have decided to provide an italian translation of the tarc's texts. 2 even though in the last few years ads have received an increasing attention by the nlp community, many aspects have not been studied yet and one of these is the arabish code-system. the first reason for this lack of research is the relatively recent widespread of its use: before the advent of the social media, arabish usage was basically confined to text messaging. however, the landscape has changed considerably, and particularly thanks to the massive registration of users on facebook since 2008. at that time, in tunisia there were still no arabic keyboards, neither for personal computers, nor for phones, so arabic-speaking users designed ta for writing in social media ( arabic writing in arabic characters: coda (conventional orthography for dialectal arabic) @xcite . the remainder of the paper is organized as follows: section 2 is an overview of nlp studies on tun and ta; section 3 describes tun and ta; section 4 presents the tarc corpus building process; section 5 explains preliminary experiments with a semi-automatic transcription and annotation procedure, adopted for a faster and simpler construction of the tarc corpus; conclusions are drawn in section 6. ## related work in this section, we provide an overview of work done on automatic processing of tun and ta. as briefly outlined above, many studies on tun and ta aim at solving the lack of standard orthography. the first conventional orthography for dialectal arabic (coda) was for egyptian arabic @xcite and it was used by @xcite for egyptian arabish transliteration into arabic script. the coda version for tun (coda tun) was developed by @xcite , and was used in many studies, like @xcite . such work presents a research on automatic word recognition in tun. narrowing down to the specific field of ta, coda tun was used in @xcite to realize a ta-arabic script conversion tool, implemented with a rule-based approach. the most extensive coda is coda*, a unified set of guidelines for 28 arab city dialects @xcite . for the present research, coda* is considered the most convenient guideline to follow due to its extensive applicability, which will support comparative studies of corpora in different ads. as we already mentioned, there are few nlp tools available for arabish processing in comparison to the amount of nlp tools realized for arabic. considering the lack of spelling conventions for arabish, previous effort has focused on automatic transliteration from arabish to arabic script, e.g. @xcite , darwish (2013), and @xcite . these three work are based on a character-to-character mapping model that aims at generating a range of alternative words that must then be selected through a linguistic model. a different method is presented in @xcite , in which the authors present a sequence-to-sequence-based approach for ta-arabic characters transliteration in both directions @xcite . regardless of the great number of work done on tun automatic processing, there are not a lot of tun corpora available for free @xcite . to the best of our knowledge there are only five tun corpora freely downloadable: one of these is the padic @xcite , composed of 6,400 sentences in six arabic dialects, translated in modern standard arabic (msa), and annotated at sentence level. 3 two other corpora are the tunisian dialect corpus interlocutor (tudicoi) @xcite and the spoken tunisian arabic corpus (stac) @xcite , which are both morpho-syntactically annotated. the first one is a spoken task-oriented dialogue corpus, which gathers a set of conversations between staff and clients recorded in a railway station. tudicoi consists of 21,682 words in client turns @xcite . 4 the stac is composed of 42,388 words collected from audio files downloaded from the web (as tv channels and radio stations files) @xcite . a different corpus is the taric @xcite , which contains 20 hours of tun speech, transcribed in arabic characters @xcite . 5 the last one is the tsac @xcite , containing 17k comments from facebook, manually annotated to positive and negative polarities @xcite . this corpus is the only one that contains ta texts as well as texts in arabic characters. as far as we know there are no available corpora of ta transcribed in arabic characters which are also morpho-syntactically annotated. in order to provide an answer to the lack of resources for ta, we decided to create tarc, a corpus entirely dedicated to the ta writing system, transcribed in coda tun and provided with a lemmatization level and pos tag annotation. ## characteristics of tunisian arabic and tunisian arabish the tunisian dialect (tun) is the spoken language of tunisian everyday life, commonly referred to as ‫ة‬ َ ‫ِج‬ ‫ار‬ ‫,الد‬ ad-dārija, ‫ة‬ ‫ي‬ ‫ام‬ َ ‫,الع‬ al-'āmmiyya, or ‫ي‬ ِ ‫ْس‬ ‫ون‬ ‫,الت‬ ət-tūnsī. according to the traditional diatopic classification, tun belongs to the area of maghrebi arabic, of which the other main varieties are libyan, algerian, moroccan and the ḥassānīya variety of mauritania foot_3 @xcite . arabish is the transposition of ads, which are mainly spoken systems, into written form, thus turning into a quasi-oral system (this topic will be discussed in section 3.2). in addition, arabish is not realized through arabic script and consequently it is not subject to the standard arabic orthographic rules. as a result, it is possible to consider ta as a faithful written representation of the spoken tun @xcite . ## tunisian arabish corpus in order to analyze the ta system, we have built a ta corpus based on social media data, considering this as the best choice to observe the quasi-oral nature of the ta system. 13 regarding the last two phenomena, they can be visible in arabic script only in case of texts provided with short vowels, which are quite rare. ## incremental and semi-automatic transcription in order to make the corpus collection easier and faster, we adopted a semi-automatic procedure based on sequential neural models @xcite . since transcribing arabish into arabic is by far the most important information to study the arabish code-system, the semiautomatic procedure concerns only transcription from arabish to arabic script. in order to proceed, we used the first group of (roughly) 6,000 manually transcribed tokens as training and test data sets in a 10-fold cross validation setting with 9-1 proportions for training and test, respectively. as we explained in the previous section, french tokens were removed from the data. more precisely, whole sentences containing non-transcribable french tokens (code-switching) were removed from the data. since at this level there is no way for predicting when a french word can be transcribed into arabic and when it has to be left unchanged, french tokens create some noise for an automatic, probabilistic model. after removing sentences with french tokens, the data reduced to roughly 5,000 tokens. we chose this amount of tokens for annotation blocks in our incremental annotation procedure. we note that by combining sentence, paragraph and token index in the corpus, whole sentences can be reconstructed. however, from 5,000 tokens roughly 300 sentences could be reconstructed, which are far too few to be used for training a neural model. 16 instead, since tokens are transcribed at morpheme level, we split arabish tokens into characters, and arabic tokens into morphemes, and we treated each token itself as a sequence. our model learns thus to map arabish characters into arabic morphemes. the 10-fold cross validation with this setting gave a token-level accuracy of roughly 71%. this result is not satisfactory on an absolute scale, however it is more than encouraging taking into account the small size of our data. this result means that less than 3 tokens, on average, out of 10, must be corrected to increase the size of our corpus. with this model we automatically transcribed into arabic morphemes, roughly, 5,000 additional tokens, corresponding to the second annotation block. this can be manually annotated in at least 7,5 days, but thanks to the automatic annotation accuracy, it was manually corrected into 3 days. 17 the accuracy of the model on the annotation of the second block was roughly 70%, which corresponds to the accuracy on the test set. the manually-corrected additional tokens were added to the training data of our neural model, and a new block was automatically annotated and manually corrected. both accuracy on the test set and on the annotation block remained at around 70%. this is because the block added to the training data was significantly different from the previous and from the third. adding the third block to the training data and annotating a fourth block with the new trained model gave in contrast an accuracy of roughly 80%. this incremental, semi-automatic transcription procedure is in progress for the remaining blocks, but it is clear that it will make the corpus annotation increasingly easier and faster as the amount of training data will grow up. our goal concerning transcription, is to have the 25,000 tokens mentioned in section 4.1 annotated automatically and manually corrected. these data will constitute our gold annotated data, and they will be used to automatically transcribe further data. ## conclusions in this paper we presented tarc, the first tunisian arabish corpus annotated with morpho-syntactic information. we discussed the decisions taken in order to highlight the phonological and morphological features of tun through the ta corpus structure. concerning the building process, we have shown the steps undertaken and our effort intended to make the corpus as representative as possible of ta. we therefore described the texts collection stage, as well as the corpus building and the semi-automatic procedure adopted for transcribing ta into arabic script, taking into account coda* and coda tun guidelines. at the present stage of research, tarc consists of 25.000 tokens, however our work is in progress and for future research we plan to enforce the semi-automatic transcription, which has already shown encouraging results (@xmath0. we also intend to realize a semiautomatic ta part-of-speech tagger. thus, we aim to develop tools for ta processing and, in so doing, we strive to complete the annotation levels (transcription, pos tag, lemmatization) semi-automatically in order to increase the size of the corpus, making it available for linguistic analyses on ta and tun.