Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2021

Abstract

Recurrent Neural Networks (RNNs) have demonstrated their effectiveness in learning and processing sequential data (e.g., speech and natural language). However, due to the black-box nature of neural networks, understanding the decision logic of RNNs is quite challenging. Some recent progress has been made to approximate the behavior of an RNN by weighted automata. They provide better interpretability, but still suffer from poor scalability. In this paper, we propose a novel approach to extracting weighted automata with the guidance of a target RNN’s decision and context information. In particular, we identify the patterns of RNN’s step-wise predictive decisions to instruct the formation of automata states. Further, we propose a state composition method to enhance the context-awareness of the extracted model. Our in-depth evaluations on typical RNN tasks, including language model and classification, demonstrate the effectiveness and advantage of our method over the state-of-the-arts. The evaluation results show that our method can achieve accurate approximation of an RNN even on large-scale tasks.

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, February 2-9,

Volume

35

Issue

13

First Page

11699

Last Page

11707

Publisher

AAAI Press

City or Country

Virtual Conference

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