Large-Scale QA-SRL Parsing
Nicholas FitzGerald Julian Michael∗ Luheng He Luke Zettlemoyer∗ Paul G. Allen School of Computer Science and Engineering University of Washington, Seattle, WA Much of this work was done while these authors were at the Allen Institute for Artificial Intelligence.
Abstract
We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.
中文速览
传统的语义角色标注（Semantic Role Labeling）需要专业语言学家按照厚达上百页的标注规范来操作，成本高、规模小。这篇论文提出用"问答驱动的语义角色标注"（QA-SRL）来替代，让普通众包工人通过写问答对（例如"谁做了什么、在哪里、对谁"）来标注句子中的谓词-论元关系，并设计了一套带自动补全和验证环节的众包流程，以9天时间、约4.4万美元的成本，在三个领域的6.4万句子上收集了超过26万条高质量问答对，构建了迄今最大的QA-SRL语料库QA-SRL Bank 2.0。在此基础上，作者训练了第一个高质量的QA-SRL解析器，采用基于跨度（span-based）的论元检测模型和LSTM问题生成模型，最终在人工评估中实现了82.6%的问题准确率和77.6%的跨度精度。这项工作证明了大规模、低成本地获取语义结构标注是可行的，为后续语义解析、开放信息抽取等下游任务提供了重要的数据和模型基础。
原文 arXiv:1805.05377;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1805.05377v1