Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules. Our implementation can be found at https://github.com/FredericGodin/ContextualDecomposition-NLP .

本文研究字符级别模式识别神经网络所学习的规律以及相对于手动标注的词语分割的重叠性，并使用卷积神经网络和双向长短期记忆网络对三种不同的语言进行形态标记任务的评估和比较，证明这些模型可以隐式地发现可理解的语言规则。

使用字符感知神经网络进行词级别预测：它们是否发现语言规则？