# Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection

arXiv: https://arxiv.org/abs/2103.09519 · HTML: https://mlsanity.com/paper/2103.09519/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models](https://mlsanity.com/paper/2006.15994/) (2020)
- [Aspect Category Detection via Topic-Attention Network](https://mlsanity.com/paper/1901.01183/) (2019)
- [An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure](https://mlsanity.com/paper/1812.03361/) (2018)

## Built on by (most cited first, 4 of 4)

- [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://mlsanity.com/paper/2109.09701/) (2021)
- [SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese](https://mlsanity.com/paper/2209.10482/) (2022)
- [Detecting Spam Reviews on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2207.14636/) (2022)
- [Predicting Job Titles from Job Descriptions with Multi-label Text Classification](https://mlsanity.com/paper/2112.11052/) (2021)

## Abstract (arXiv)

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.