@inproceedings{titung-alm-2022-teaching,
    title = "Teaching Interactively to Learn Emotions in Natural Language",
    author = "Titung, Rajesh  and
      Alm, Cecilia",
    editor = "Blodgett, Su Lin  and
      Daum{\'e} III, Hal  and
      Madaio, Michael  and
      Nenkova, Ani  and
      O'Connor, Brendan  and
      Wallach, Hanna  and
      Yang, Qian",
    booktitle = "Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.hcinlp-1.6/",
    doi = "10.18653/v1/2022.hcinlp-1.6",
    pages = "40--46",
    abstract = "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."
}