Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.
翻译:自然语言处理(NLP)可以采用自上而下(理论驱动)和自下而上(数据驱动)两种方法,我们分别称之为机械学和苯蛋白学,这些方法常常被认为相互对立。研究一些最近的深层次学习方法,我们认为深层神经网络既包含视角,又包含深层学习的这一方面可能有助于解决语言技术内部的复杂问题,例如模拟语言和空间认知领域的认知。