  This paper describes the submissions by team HWR to the Dravidian Language
Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI
training set includes 16,674 YouTube comments written in Roman script
containing code-mixed text with English and one of the three South Dravidian
languages: Kannada, Malayalam, and Tamil. We submitted results generated using
two models, a Naive Bayes classifier with adaptive language models, which has
shown to obtain competitive performance in many language and dialect
identification tasks, and a transformer-based model which is widely regarded as
the state-of-the-art in a number of NLP tasks. Our first submission was sent in
the closed submission track using only the training set provided by the shared
task organisers, whereas the second submission is considered to be open as it
used a pretrained model trained with external data. Our team attained shared
second position in the shared task with the submission based on Naive Bayes.
Our results reinforce the idea that deep learning methods are not as
competitive in language identification related tasks as they are in many other
text classification tasks.
