Machine Learning Won't Solve Natural Language Understanding
An argument that it is time to re-think our approach to natural language understanding, since the ‘big data’ approach to NLU is implausible and flawed..
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.