@inproceedings{2d524a00c11045e3b9ef5485e91c7551,
title = "Explaining Neural NLP Models for the Joint Analysis of Open- and Closed-Ended Survey Answers",
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.",
author = "Edoardo Mosca and Katharina Hermann and Tobias Eder and Georg Groh",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2nd Workshop on Trustworthy Natural Language Processing, TrustNLP 2022 ; Conference date: 14-07-2022",
year = "2022",
language = "English",
series = "TrustNLP 2022 - 2nd Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "49--63",
editor = "Apurv Verma and Yada Pruksachatkun and Kai-Wei Chang and Aram Galstyan and Jwala Dhamala and Cao, {Yang Trista}",
booktitle = "TrustNLP 2022 - 2nd Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",
}