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首页> 外文会议>Annual meeting of the Association for Computational Linguistics >Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
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Deep Neural Model Inspection and Comparison via Functional Neuron Pathways

机译：通过功能神经元通路的深神经模型检查和比较

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摘要

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.
机译：我们介绍了神经模型的解释和比较的一般方法。该方法用于将复杂的神经模型分解成其功能组分，其包括跨网络架构层的共同发射神经元组成，以及我们称之为神经途径。通过识别相关的任务水平和语言启发式可以理解这些途径的功能，即这种知识作为镜头近似于网络已经学会了应用于其预期任务的镜头。作为调查这些途径的效用的案例研究，我们展示了对培训的模型中确定的途径检查，即命名实体识别并识别文本征征。

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