# ComOM at VLSP 2023: A Dual-Stage Framework with BERTology and Unified Multi-Task Instruction Tuning Model for Vietnamese Comparative Opinion Mining

arXiv: https://arxiv.org/abs/2312.09000 · HTML: https://mlsanity.com/paper/2312.09000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation](https://mlsanity.com/paper/2205.06457/) (2022)
- [PhoGPT: Generative Pre-training for Vietnamese](https://mlsanity.com/paper/2311.02945/) (2023)
- [Comparative Opinion Mining: A Review](https://mlsanity.com/paper/1712.08941/) (2017)

## Built on by (most cited first, 1 of 1)

- [Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning](https://mlsanity.com/paper/2412.08508/) (2024)

## Abstract (arXiv)

The ComOM shared task aims to extract comparative opinions from product reviews in Vietnamese language. There are two sub-tasks, including (1) Comparative Sentence Identification (CSI) and (2) Comparative Element Extraction (CEE). The first task is to identify whether the input is a comparative review, and the purpose of the second task is to extract the quintuplets mentioned in the comparative review. To address this task, our team proposes a two-stage system based on fine-tuning a BERTology model for the CSI task and unified multi-task instruction tuning for the CEE task. Besides, we apply the simple data augmentation technique to increase the size of the dataset for training our model in the second stage. Experimental results show that our approach outperforms the other competitors and has achieved the top score on the official private test.