The Role of Explanatory Value in Natural Language Processing

Before explaining what I mean, let me set the scene. Broadly, NLP can be pursued in three mutually overlapping ways, which emphasize different aspects of our work. First, there is NLP as Engineering, where NLP models are built primarily to serve some practical goal, answering a need that exists in society. Second, there is what might be called NLP-as-Mathematics, which studies algorithms and models in their own right, comparing them and developing new ones. Finally, there is NLP-as-Science, where models are constructed with the aim of expressing, testing, and ultimately enhancing humankind's grasp of human language and language use, because computational models offer a level of explicitness and detail that other theories of language often lack.