@inproceedings{fiacco-etal-2019-deep,
title = "Deep Neural Model Inspection and Comparison via Functional Neuron Pathways",
author = "Fiacco, James and
Choudhary, Samridhi and
Rose, Carolyn",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1575",
doi = "10.18653/v1/P19-1575",
pages = "5754--5764",
abstract = "We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.",
}
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%0 Conference Proceedings
%T Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
%A Fiacco, James
%A Choudhary, Samridhi
%A Rose, Carolyn
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F fiacco-etal-2019-deep
%X We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.
%R 10.18653/v1/P19-1575
%U https://aclanthology.org/P19-1575
%U https://doi.org/10.18653/v1/P19-1575
%P 5754-5764
Markdown (Informal)
[Deep Neural Model Inspection and Comparison via Functional Neuron Pathways](https://aclanthology.org/P19-1575) (Fiacco et al., ACL 2019)
ACL