@inproceedings{kumar-etal-2019-didnt,
title = "Why Didn{'}t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models",
author = "Kumar, Varun and
Smith-Renner, Alison and
Findlater, Leah and
Seppi, Kevin and
Boyd-Graber, Jordan",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1637",
doi = "10.18653/v1/P19-1637",
pages = "6323--6330",
abstract = "To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations{'} results match users{'} expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.",
}
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%0 Conference Proceedings
%T Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
%A Kumar, Varun
%A Smith-Renner, Alison
%A Findlater, Leah
%A Seppi, Kevin
%A Boyd-Graber, Jordan
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kumar-etal-2019-didnt
%X To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations’ results match users’ expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.
%R 10.18653/v1/P19-1637
%U https://aclanthology.org/P19-1637
%U https://doi.org/10.18653/v1/P19-1637
%P 6323-6330
Markdown (Informal)
[Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models](https://aclanthology.org/P19-1637) (Kumar et al., ACL 2019)
ACL