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        <title>Teaching Interactively to Learn Emotions in Natural Language</title>
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        <namePart type="given">Rajesh</namePart>
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            <namePart type="given">Su</namePart>
            <namePart type="given">Lin</namePart>
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            <namePart type="given">Hal</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Teaching Interactively to Learn Emotions in Natural Language
%A Titung, Rajesh
%A Alm, Cecilia
%Y Blodgett, Su Lin
%Y Daumé III, Hal
%Y Madaio, Michael
%Y Nenkova, Ani
%Y O’Connor, Brendan
%Y Wallach, Hanna
%Y Yang, Qian
%S Proceedings of the Second Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F titung-alm-2022-teaching
%X 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.
%R 10.18653/v1/2022.hcinlp-1.6
%U https://aclanthology.org/2022.hcinlp-1.6
%U https://doi.org/10.18653/v1/2022.hcinlp-1.6
%P 40-46

Markdown (Informal)

[Teaching Interactively to Learn Emotions in Natural Language](https://aclanthology.org/2022.hcinlp-1.6) (Titung & Alm, HCINLP 2022)

• Teaching Interactively to Learn Emotions in Natural Language (Titung & Alm, HCINLP 2022)

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.