Teaching Interactively to Learn Emotions in Natural Language

Rajesh Titung, Cecilia Alm


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.hcinlp-1.6.pdf
Video:
 https://aclanthology.org/2022.hcinlp-1.6.mp4