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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Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
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.
more »
« less
- NSF-PAR ID:
- 10174703
- Date Published:
- Journal Name:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Page Range / eLocation ID:
- 5754–5764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/P19-1575
