Conditional random fields (CRFs), a particular type of graph neural networks (GNNs), can be used to make structured predictions in machine learning, with various applications from image processing and natural language processing to recommender systems. CRFs refine the prediction of a sample by taking into account its context information. However, there is a lack of work on post-hoc explanation approaches to CRFs, especially when the model is softmax-activated like the deep mean field network (DMFN). In this paper, we bridge this gap by proposing a layer-wise relevance propagation (LRP) method based on deep Taylor decomposition to explain CRFs, especially the DMFN model. The method considers the intermediate softmax activation layers in DMFN. We use two evaluation settings: top K\% deletion and insertion to evaluate the method. Experimental studies on fake news detection using the DMFN model prove the effectiveness of our explanation method compared to the other baseline methods.
Yang, X, Joukovsky, B & Deligiannis, N 2023, Relevance Propagation through Deep Conditional Random Fields. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, IEEE, pp. 1-5, 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10095075
Yang, X., Joukovsky, B., & Deligiannis, N. (2023). Relevance Propagation through Deep Conditional Random Fields. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1-5). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). IEEE. https://doi.org/10.1109/ICASSP49357.2023.10095075
@inproceedings{f09a8f8dc12840c39fc24cd809a782e1,
title = "Relevance Propagation through Deep Conditional Random Fields",
abstract = "Conditional random fields (CRFs), a particular type of graph neural networks (GNNs), can be used to make structured predictions in machine learning, with various applications from image processing and natural language processing to recommender systems. CRFs refine the prediction of a sample by taking into account its context information. However, there is a lack of work on post-hoc explanation approaches to CRFs, especially when the model is softmax-activated like the deep mean field network (DMFN). In this paper, we bridge this gap by proposing a layer-wise relevance propagation (LRP) method based on deep Taylor decomposition to explain CRFs, especially the DMFN model. The method considers the intermediate softmax activation layers in DMFN. We use two evaluation settings: top K\% deletion and insertion to evaluate the method. Experimental studies on fake news detection using the DMFN model prove the effectiveness of our explanation method compared to the other baseline methods.",
author = "Xiangyu Yang and Boris Joukovsky and Nikos Deligiannis",
note = "Funding Information: This research received funding from the Flemish Government under the âOnderzoeksprogramma Artifici{\"e}le Intelligentie (AI) Vlaanderenâ programme, and from the FWO (Grant 1SB5721N), Belgium. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
month = may,
day = "5",
doi = "10.1109/ICASSP49357.2023.10095075",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",
pages = "1--5",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing",
url = "https://2023.ieeeicassp.org/",
}