@inproceedings{e9a096d46d1f400aa91577dadd65ed56,
title = "An Experimental Study on the Interpretability of Transformer Models for Dialog Understanding",
abstract = "Identifying the intention of an utterance (e.g., spoken sentence) is part of semantic understanding. Transformer models offer promising performance for intention identification, however model interpretability remains low. This paper presents a comprehensive experimental study on the interpretability of BERT-like models, such as DistilBERT, for spoken dialog intention understanding. A detailed discussion explains the main features used in intention classification. This insight can be a starting point to devise more interpretable transformer models.",
keywords = "experimental study, interpretability, problem solving, spoken dialog understanding, transformer model",
author = "Gnaneswar Villuri and Alex Doboli",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 3rd IEEE Conference on Artificial Intelligence, CAI 2025 ; Conference date: 05-05-2025 Through 07-05-2025",
year = "2025",
doi = "10.1109/CAI64502.2025.00043",
language = "English",
series = "Proceedings - 2025 IEEE Conference on Artificial Intelligence, CAI 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "229--235",
booktitle = "Proceedings - 2025 IEEE Conference on Artificial Intelligence, CAI 2025",
}