Teaching Interactively to Learn Emotions in Natural Language
Rajesh Titung, Cecilia Ovesdotter Alm · 2022
Motivated by prior literature, we provide a proof of concept simulation study for an understudied interactive machine learning method, machine teaching (MT), for the text-based emotion prediction task.We compare this method experimentally against a more well-studied technique, active learning (AL).Results show the strengths of both approaches over more resource-intensive offline supervised learning.Additionally, applying AL and MT to fine-tune a pre-trained model offers further efficiency gain.We end by recommending research directions which aim to empower users in the learning process.