  Aspect category detection (ACD) is one of the challenging tasks in the
Aspect-based sentiment Analysis problem. The purpose of this task is to
identify the aspect categories mentioned in user-generated reviews from a set
of pre-defined categories. In this paper, we investigate the performance of
various monolingual pre-trained language models compared with multilingual
models on the Vietnamese aspect category detection problem. We conduct the
experiments on two benchmark datasets for the restaurant and hotel domain. The
experimental results demonstrated the effectiveness of the monolingual PhoBERT
model than others on two datasets. We also evaluate the performance of the
multilingual model based on the combination of whole SemEval-2016 datasets in
other languages with the Vietnamese dataset. To the best of our knowledge, our
research study is the first attempt at performing various available pre-trained
language models on aspect category detection task and utilize the datasets from
other languages based on multilingual models.
