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

Title: 可扩展的数据驱动理论的案例：自然语言处理科学进步的范式

Abstract: 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. Show Chinese abstract

Abstract: 我提出了一种围绕开发可扩展、数据驱动的语言结构理论的自然语言处理科学进步范式。 想法是通过严格限定范围和精心定义的方式收集数据，从而对感兴趣的行为现象进行全面注释，然后使用机器学习构建这些现象的解释性理论，这些理论可以作为可理解的人工智能系统的构建模块。 在建立一些概念基础之后，我描述了几项关于使用问题回答驱动的语义角色标注（QA-SRL）的数据驱动浅层语义结构的研究，这是一种使用高度受限的问题回答对来注释动词谓词-论元关系的方案。 虽然这仅触及了人工智能中感兴趣的复杂语言行为的一小部分，但我概述了数据收集和理论建模的原则，这些原则可以指导未来的科学进展。 本笔记总结并大量参考了我的博士论文。