Palomino-ochoa at tass 2020: Transformer-based data augmentation for overcoming few-shot learning

Palominoa D.; Ochoa-Luna J. · CEUR Workshop Proceedings (2020)

Resumen

This paper describes the participation of the Department of Computer Science at Universidad Católica San Pablo (UCSP) for the TASS 2020 Workshop. We have developed sentiment analysis algorithms for the monovariant and multivariant challenges. In both cases, our approach is based on transfer learning using BERT language modeling. We also propose a procedure based on this language model to generate contextual data augmentation aimed to increase the training dataset and prevent overfitting. Our design choices allow us to achieve comparable state-of-the-art results regarding the TASS benchmark datasets provided.

Instituciones: Universidad Católica San Pablo