ComOM at VLSP 2023: A Dual-Stage Framework with BERTology and Unified
  Multi-Task Instruction Tuning Model for Vietnamese Comparative Opinion Mining
Dang Van Thin Duong Ngoc Hao Ngan Luu-Thuy Nguyen
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.