@inproceedings{lee-etal-2021-micromodels-efficient,
title = "Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health",
author = "Lee, Andrew and
Kummerfeld, Jonathan K. and
An, Larry and
Mihalcea, Rada",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.360",
doi = "10.18653/v1/2021.findings-emnlp.360",
pages = "4257--4272",
abstract = "Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model{'}s decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.",
}
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<abstract>Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model’s decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.</abstract>
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%0 Conference Proceedings
%T Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health
%A Lee, Andrew
%A Kummerfeld, Jonathan K.
%A An, Larry
%A Mihalcea, Rada
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F lee-etal-2021-micromodels-efficient
%X Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model’s decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.
%R 10.18653/v1/2021.findings-emnlp.360
%U https://aclanthology.org/2021.findings-emnlp.360
%U https://doi.org/10.18653/v1/2021.findings-emnlp.360
%P 4257-4272
Markdown (Informal)
[Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health](https://aclanthology.org/2021.findings-emnlp.360) (Lee et al., Findings 2021)
ACL