@inproceedings{mosca-etal-2022-explaining,
title = "Explaining Neural {NLP} Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers",
author = "Mosca, Edoardo and
Harmann, Katharina and
Eder, Tobias and
Groh, Georg",
editor = "Verma, Apurv and
Pruksachatkun, Yada and
Chang, Kai-Wei and
Galstyan, Aram and
Dhamala, Jwala and
Cao, Yang Trista",
booktitle = "Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)",
month = jul,
year = "2022",
address = "Seattle, U.S.A.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.trustnlp-1.5",
doi = "10.18653/v1/2022.trustnlp-1.5",
pages = "49--63",
abstract = "Large-scale surveys are a widely used instrument to collect data from a target audience. Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers. Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment. This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open- and closed-ended questions. We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model. Experiments on EMS{---}a survey-based study researching influencing factors affecting a student{'}s career goals{---}show that the proposed approach can identify such factors both at the input- and higher concept-level.",
}
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%0 Conference Proceedings
%T Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers
%A Mosca, Edoardo
%A Harmann, Katharina
%A Eder, Tobias
%A Groh, Georg
%Y Verma, Apurv
%Y Pruksachatkun, Yada
%Y Chang, Kai-Wei
%Y Galstyan, Aram
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%S Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, U.S.A.
%F mosca-etal-2022-explaining
%X Large-scale surveys are a widely used instrument to collect data from a target audience. Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers. Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment. This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open- and closed-ended questions. We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model. Experiments on EMS—a survey-based study researching influencing factors affecting a student’s career goals—show that the proposed approach can identify such factors both at the input- and higher concept-level.
%R 10.18653/v1/2022.trustnlp-1.5
%U https://aclanthology.org/2022.trustnlp-1.5
%U https://doi.org/10.18653/v1/2022.trustnlp-1.5
%P 49-63
Markdown (Informal)
[Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers](https://aclanthology.org/2022.trustnlp-1.5) (Mosca et al., TrustNLP 2022)
ACL