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

我提出了一种以发展可扩展的、数据驱动的语言结构理论为核心的 NLP 科学进展的理念，该理念在于以严格限定的方式收集数据，并通过机器学习构建解释性理论，以构建可理解的 AI 系统的基础。本文介绍了使用基于问题 - 答案的语义角色标注 (QA-SRL) 对浅层语义结构进行数据驱动理论研究的几个调查，以及收集数据和理论建模的原则，这些原则能够指导未来的科学进展。