Leveraging Transfer Learning for Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL)
This work addresses the problem of identifying reliable intelligence on Vietnamese social media, which is an incremental contribution to the field of natural language processing for Vietnamese.
This paper explored transformer-based methods, including monolingual and multilingual pre-trained models and ensemble techniques, for identifying reliable intelligence on Vietnamese social network sites. The team achieved a competitive ROC-AUC score of 0.9378 on the private test set.
This paper proposed several transformer-based approaches for Reliable Intelligence Identification on Vietnamese social network sites at VLSP 2020 evaluation campaign. We exploit both of monolingual and multilingual pre-trained models. Besides, we utilize the ensemble method to improve the robustness of different approaches. Our team achieved a score of 0.9378 at ROC-AUC metric in the private test set which is competitive to other participants.