Machine Learning Won't Solve Natural Language Understanding

In the early 1990s a statistical revolution overtook artificial intelligence (AI) by a storm – a revolution that culminated by the 2000's in the triumphant return of neural networks with their modern-day deep learning (DL) reincarnation. This empiricist turn engulfed all subfields of AI although the most controversial employment of this technology has been in natural language processing (NLP) – a subfield of AI that has proven to be a lot more difficult than any of the AI pioneers had imagined. The widespread use of data-driven empirical methods in NLP has the following genesis: the failure of the symbolic and logical methods to produce scalable NLP systems after three decades of supremacy led to the rise of what are called empirical methods in NLP (EMNLP) – a phrase that I use here to collectively refer to data-driven, corpus-based, statistical and machine learning (ML) methods. The motivation behind this shift to empiricism was quite simple: until we gain some insights in how language works and how language is related to our knowledge of the world we talk about in ordinary spoken language, empirical and data-driven methods might be useful in building some practical text processing applications. As Kenneth Church, one of the pioneers of EMNLP explains, the advocates of the data-driven and statistical approaches to NLP were interested in solving simple language tasks – the motivation was never to suggest that this is how language works, but that "it is better to do something simple than nothing at all".