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

• URL: http://arxiv.org/abs/2312.09000v1
• Date: Thu, 14 Dec 2023 14:44:59 GMT
• Title: ComOM at VLSP 2023: A Dual-Stage Framework with BERTology and Unified Multi-Task Instruction Tuning Model for Vietnamese Comparative Opinion Mining
• Authors: Dang Van Thin, Duong Ngoc Hao, Ngan Luu-Thuy Nguyen
• Abstract summary: The ComOM shared task aims to extract comparative opinions from product reviews in Vietnamese language. We propose 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. Experimental results show that our approach outperforms the other competitors and has achieved the top score on the official private test.
• Score: 0.6522338519818377
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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