Two-stage Pipeline for Multilingual Dialect Detection (2023)

Abstract: Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively. Our proposed approach consists of a two-stage system and outperforms other participants’ systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly ( https://github.com/ankit-vaidya19/EACL_VarDial2023 ).

## introduction language has been the primary mode of communication for humans since the pre-historic ages. studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language @xcite . inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region. this gave rise to multiple national dialects of the same language. the vardial workshop @xcite (colocated with eacl 2023) explores various dialects and variations of the same language. we participated in the discriminating between similar languages -true labels (dsl-tl) shared task. in this task, the participants were provided with data from three languages, with each language having three varieties. this shared task consisted of two tracks -track-1 featuring nine-way classification and track-2 featuring six-way classification. the second track included two particular national dialects of each language (eg. american english and british english), and the first track had one general 1 https://github.com/ankit-vaidya19/eacl_ vardial2023 label (english) in addition to the aforementioned two. we ranked 1 st in both of the tracks. moreover, we beat the next best submission by a margin of 4.5% in the first task and 5.6% in the second task.we were the only team to surpass the organizer baseline scores. we present our winning solution in this paper. we used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language. we converged upon the best combination by doing an elaborate analysis of various models available. furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study. lastly, we provide some future directions in this area of research. ## related work the present literature encompasses various aspects of dialect identification. we study this from three perspectives: large language models, language identification and dialect classification problems. ## system description this was a problem of multi-class classification having 9 classes for track-1 and 6 classes for track-2. the samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages. the language identification (lid) model which is the first stage classifies the sentence into 3 languages: english (en), spanish (es) and portuguese (pt). the lid is a pretrained xlm-roberta that is fine-tuned for the task of language identification. it is able to classify the input sentence into 20 languages. we classify and separate the samples according to their language. the samples corresponding to the specific languages are then fed into the language specific models for dialect identification. for dialect identification we have used models like bert and roberta with a linear layer connected to the pooler output of the models. then fine-tuning is done on the models for dialect identification using the samples corresponding to the specific languages. for the task of dialect identification we experimented with several pretrained models ## conclusion in this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora. we conduct thorough ablations on this setup and provide valuable insights. we foresee multiple future directions for this work. the first is to expand this work to many languages and dialects. secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads.