  In this work, we introduce the methods proposed by the UnibucKernel team in
solving the Social Media Variety Geolocation task featured in the 2020 VarDial
Evaluation Campaign. We address only the second subtask, which targets a data
set composed of nearly 30 thousand Swiss German Jodels. The dialect
identification task is about accurately predicting the latitude and longitude
of test samples. We frame the task as a double regression problem, employing a
variety of machine learning approaches to predict both latitude and longitude.
From simple models for regression, such as Support Vector Regression, to deep
neural networks, such as Long Short-Term Memory networks and character-level
convolutional neural networks, and, finally, to ensemble models based on
meta-learners, such as XGBoost, our interest is focused on approaching the
problem from a few different perspectives, in an attempt to minimize the
prediction error. With the same goal in mind, we also considered many types of
features, from high-level features, such as BERT embeddings, to low-level
features, such as characters n-grams, which are known to provide good results
in dialect identification. Our empirical results indicate that the handcrafted
model based on string kernels outperforms the deep learning approaches.
Nevertheless, our best performance is given by the ensemble model that combines
both handcrafted and deep learning models.
