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I propose a paradigm for scientific progress in NLP centered arounddeveloping scalable, data-driven theories of linguistic structure. The idea isto collect data in tightly scoped, carefully defined ways which allow forexhaustive annotation of behavioral phenomena of interest, and then use machinelearning to construct explanatory theories of these phenomena which can formbuilding blocks for intelligible AI systems. After laying some conceptualgroundwork, I describe several investigations into data-driven theories ofshallow semantic structure using Question-Answer driven Semantic Role Labeling(QA-SRL), a schema for annotating verbal predicate-argument relations usinghighly constrained question-answer pairs. While this only scratches the surfaceof the complex language behaviors of interest in AI, I outline principles fordata collection and theoretical modeling which can inform future scientificprogress. This note summarizes and draws heavily on my PhD thesis.

The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP