对比三种生成事件描述的方法，发现人工仍更自然准确。
Generating event descriptions under syntactic and semantic constraints
- 对比专家人工、语料采样和语言模型三种生成方式。
- 三类方法均生成自然、典型且独特的事件描述。
- 适合需要高质量事件描述的语义标注与分析任务。
为支持可扩展的词汇语义标注、分析与理论构建，我们全面评估了在句法约束（如期望的从句结构）和语义约束（如期望的动词义项）下生成事件描述的不同方法。比较了三种方法：(i) 专家人工生成；(ii) 从标注了句法与语义信息的语料中采样；(iii) 从条件于句法与语义信息的语言模型中采样。从三个维度评估生成的事件描述：(a) 自然度，(b) 典型性，(c) 独特性。结果表明，所有方法均能可靠生成自然、典型且独特的事件描述，但人工生成仍优于自动化方法。结论是，所考察的自动化方法生成的描述质量足以用于下游标注与分析，前提是相关方法对描述中轻微退化具有鲁棒性。
原文摘要 · Abstract (English)
With the goal of supporting scalable lexical semantic annotation, analysis, and theorizing, we conduct a comprehensive evaluation of different methods for generating event descriptions under both syntactic constraints -- e.g. desired clause structure -- and semantic constraints -- e.g. desired verb sense. We compare three different methods -- (i) manual generation by experts; (ii) sampling from a corpus annotated for syntactic and semantic information; and (iii) sampling from a language model (LM) conditioned on syntactic and semantic information -- along three dimensions of the generated event descriptions: (a) naturalness, (b) typicality, and (c) distinctiveness. We find that all methods reliably produce natural, typical, and distinctive event descriptions, but that manual generation continues to produce event descriptions that are more natural, typical, and distinctive than the automated generation methods. We conclude that the automated methods we consider produce event descriptions of sufficient quality for use in downstream annotation and analysis insofar as the methods used for this annotation and analysis are robust to a small amount of degradation in the resulting event descriptions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。