ReINTEL Challenge 2020: Exploiting Transfer Learning Models for Reliable Intelligence Identification on Vietnamese Social Network Sites

TL;DR

This paper explores transfer learning models, specifically bert4news and PhoBERT, to identify reliable information on Vietnamese social networks, achieving high accuracy in a shared task dataset.

Contribution

It demonstrates the effectiveness of fine-tuning transfer learning models for reliable news detection in Vietnamese social media content.

Findings

Achieved 94.52% AUC score on the test set

Compared different transfer learning models for the task

Provided a system for reliable intelligence identification

Abstract

This paper presents the system that we propose for the Reliable Intelligence Indentification on Vietnamese Social Network Sites (ReINTEL) task of the Vietnamese Language and Speech Processing 2020 (VLSP 2020) Shared Task. In this task, the VLSP 2020 provides a dataset with approximately 6,000 trainning news/posts annotated with reliable or unreliable labels, and a test set consists of 2,000 examples without labels. In this paper, we conduct experiments on different transfer learning models, which are bert4news and PhoBERT fine-tuned to predict whether the news is reliable or not. In our experiments, we achieve the AUC score of 94.52% on the private test set from ReINTEL's organizers.