Auditing Deep Learning processes through Kernel-based Explanatory Models

Danilo Croce, Daniele Rossini, Roberto Basili

Abstract
While NLP systems become more pervasive, their accountability gains value as a focal point of effort. Epistemological opaqueness of nonlinear learning methods, such as deep learning models, can be a major drawback for their adoptions. In this paper, we discuss the application of Layerwise Relevance Propagation over a linguistically motivated neural architecture, the Kernel-based Deep Architecture, in order to trace back connections between linguistic properties of input instances and system decisions. Such connections then guide the construction of argumentations on network’s inferences, i.e., explanations based on real examples, semantically related to the input. We propose here a methodology to evaluate the transparency and coherence of analogy-based explanations modeling an audit stage for the system. Quantitative analysis on two semantic tasks, i.e., question classification and semantic role labeling, show that the explanatory capabilities (native in KDAs) are effective and they pave the way to more complex argumentation methods.
Anthology ID:
D19-1415
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4037–4046
Language:
URL:
https://aclanthology.org/D19-1415
DOI:
10.18653/v1/D19-1415
Bibkey:
Cite (ACL):
Danilo Croce, Daniele Rossini, and Roberto Basili. 2019. Auditing Deep Learning processes through Kernel-based Explanatory Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4037–4046, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Auditing Deep Learning processes through Kernel-based Explanatory Models (Croce et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/paclic-22-ingestion/D19-1415.pdf
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