Experiments in Language Variety Geolocation and Dialect Identification (2020)

Abstract: In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition of the Social Media Variety Geolocation (SMG). The submissions of our SUKI team used generative language models based on Naive Bayes and character n-grams.

## introduction we first took part in the related language identification shared tasks in 2015 @xcite and we have been using the same team name suki ever since. the shared tasks have been organized as part of the vardial workshops dealing with computational methods and language resources for closely related languages, language varieties, and dialects. the 2020 vardial evaluation campaign contained three separate shared tasks @xcite . 1 we participated in the romanian dialect identification (rdi) and the social media variety geolocation (smg) shared tasks. we did not participate in the third task, uralic language identification (uli), as we were part of the team organizing it @xcite . in this paper, we first introduce some previous work related to these shared tasks, to language identification and to identification of romanian dialects in particular as well as to geolocation of texts. then we describe the rdi and the smg shared tasks, their datasets and the systems we used in our submissions as well as the results of the shared tasks. ## romanian dialect identification (rdi) shared task the rdi shared task was a two-way classification task between texts written in the moldavian or romanian dialects of the romanian language. each participating team was allowed to submit three runs and the runs were evaluated based on the macro-averaged f 1 score. we experimented with three classification techniques and in the end we submitted only one run to the shared task gaining fourth place among the eight teams submitting results. ## social media variety geolocation (smg) shared task this task was divided into three separate tracks, each focusing on its own geographic area. the first track, de-at, included jodel conversations in standard german from germany and austria. second track, ch, focused on swiss german jodel conversations from switzerland. the third track, bcms, featured tweets from croatia, bosnia and herzegovia, montenegro, and serbia. all the tracks were scored using the median distance in kilometers of all the predicted locations to the actual ones. ## conclusions and future work in this paper, we presented the systems we experimented with when participating in two of the shared tasks organized as part of the vardial evaluation campaign 2020. our systems did not reach the state of the art in any of the tracks of the shared tasks. there is clearly some room for improvement in the approaches we used. we did not have time to experiment with using adaptive language models in the smg shared task. using them might have improved the identification accuracy considerably. also, using 100 coordinate points might not have been optimal in the deat and bcms tracks as the areas where those texts came from were larger than the ch track but we only optimized the division using the ch track training data. there is also room for improvement in how we optimized the parameters for the smg shared task as we used the average distance and the distance "unit" we used were coordinate points instead of kilometers.