Interpretable Textual Neuron Representations for NLP

Nina Poerner; Benjamin Roth; Hinrich Schütze

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arXiv · 1809.07291

Interpretable Textual Neuron Representations for NLP

Nina Poerner·Benjamin Roth·Hinrich Schütze

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Abstract

Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs. We propose and evaluate ways of transferring this technology to NLP. Our results suggest that gradient ascent with a gumbel softmax layer produces n-gram representations that outperform naive corpus search in terms of target neuron activation. The representations highlight differences in syntax awareness between the language and visual models of the Imaginet architecture.

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Nina Poerner, Benjamin Roth, Hinrich Schütze. 2018-09-19. Interpretable Textual Neuron Representations for NLP. https://arxiv.org/abs/1809.07291

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