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Anthony Bau

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AAAI Conference 2019 System Paper

NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks

  • Fahim Dalvi
  • Avery Nortonsmith
  • Anthony Bau
  • Yonatan Belinkov
  • Hassan Sajjad
  • Nadir Durrani
  • James Glass

We present a toolkit to facilitate the interpretation and understanding of neural network models. The toolkit provides several methods to identify salient neurons with respect to the model itself or an external task. A user can visualize selected neurons, ablate them to measure their effect on the model accuracy, and manipulate them to control the behavior of the model at the test time. Such an analysis has a potential to serve as a springboard in various research directions, such as understanding the model, better architectural choices, model distillation and controlling data biases. The toolkit is available for download. 1

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AAAI Conference 2019 Conference Paper

What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

  • Fahim Dalvi
  • Nadir Durrani
  • Hassan Sajjad
  • Yonatan Belinkov
  • Anthony Bau
  • James Glass

Despite the remarkable evolution of deep neural networks in natural language processing (NLP), their interpretability remains a challenge. Previous work largely focused on what these models learn at the representation level. We break this analysis down further and study individual dimensions (neurons) in the vector representation learned by end-to-end neural models in NLP tasks. We propose two methods: Linguistic Correlation Analysis, based on a supervised method to extract the most relevant neurons with respect to an extrinsic task, and Cross-model Correlation Analysis, an unsupervised method to extract salient neurons w. r. t. the model itself. We evaluate the effectiveness of our techniques by ablating the identified neurons and reevaluating the network’s performance for two tasks: neural machine translation (NMT) and neural language modeling (NLM). We further present a comprehensive analysis of neurons with the aim to address the following questions: i) how localized or distributed are different linguistic properties in the models? ii) are certain neurons exclusive to some properties and not others? iii) is the information more or less distributed in NMT vs. NLM? and iv) how important are the neurons identified through the linguistic correlation method to the overall task? Our code is publicly available as part of the NeuroX toolkit (Dalvi et al. 2019a). This paper is a non-archived version of the paper published at AAAI (Dalvi et al. 2019b).

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