@inproceedings{sultana-etal-2022-narrative,
title = "Narrative Datasets through the Lenses of {NLP} and {HCI}",
author = "Sultana, Sharifa and
Zhang, Renwen and
Lim, Hajin and
Antoniak, Maria",
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://aclanthology.org/2022.hcinlp-1.7",
doi = "10.18653/v1/2022.hcinlp-1.7",
pages = "47--54",
abstract = "In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widely-used narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.",
}
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%0 Conference Proceedings
%T Narrative Datasets through the Lenses of NLP and HCI
%A Sultana, Sharifa
%A Zhang, Renwen
%A Lim, Hajin
%A Antoniak, Maria
%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 sultana-etal-2022-narrative
%X In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widely-used narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.
%R 10.18653/v1/2022.hcinlp-1.7
%U https://aclanthology.org/2022.hcinlp-1.7
%U https://doi.org/10.18653/v1/2022.hcinlp-1.7
%P 47-54
Markdown (Informal)
[Narrative Datasets through the Lenses of NLP and HCI](https://aclanthology.org/2022.hcinlp-1.7) (Sultana et al., HCINLP 2022)
ACL
- Sharifa Sultana, Renwen Zhang, Hajin Lim, and Maria Antoniak. 2022. Narrative Datasets through the Lenses of NLP and HCI. In Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 47–54, Seattle, Washington. Association for Computational Linguistics.