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.- Anthology ID:
- 2022.hcinlp-1.6
- Volume:
- Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Su Lin Blodgett, Hal Daumé III, Michael Madaio, Ani Nenkova, Brendan O'Connor, Hanna Wallach, Qian Yang
- Venue:
- HCINLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 40–46
- Language:
- URL:
- https://aclanthology.org/2022.hcinlp-1.6
- DOI:
- 10.18653/v1/2022.hcinlp-1.6
- Cite (ACL):
- Rajesh Titung and Cecilia Alm. 2022. Teaching Interactively to Learn Emotions in Natural Language. In Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 40–46, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Teaching Interactively to Learn Emotions in Natural Language (Titung & Alm, HCINLP 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.hcinlp-1.6.pdf