论文标题
自然语言推断对称性
Inferring symmetry in natural language
论文作者
论文摘要
我们提出了一个方法论框架，用于推断自然语言中动词谓词的对称性。关于谓词对称性的经验工作采用了两种主要方法。基于特征的方法着重于与对称性有关的语言特征。基于上下文的方法否认存在绝对对称性，而是认为这种推论是上下文依赖的。我们开发了将这些方法形式化的方法，并根据新颖的对称推理句子（SIS）数据集进行了评估，该数据集由400种自然主义的文献使用动词，跨越了对称性 - 对称性的动词。我们的结果表明，将语言特征与上下文化语言模型整合在一起的混合传输学习模型最忠实地预测了经验数据。我们的工作以自然语言结合了现有的对称方法，并提出了对称推理如何改善最先进的语言模型中的系统性。
We present a methodological framework for inferring symmetry of verb predicates in natural language. Empirical work on predicate symmetry has taken two main approaches. The feature-based approach focuses on linguistic features pertaining to symmetry. The context-based approach denies the existence of absolute symmetry but instead argues that such inference is context dependent. We develop methods that formalize these approaches and evaluate them against a novel symmetry inference sentence (SIS) dataset comprised of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetry-asymmetry. Our results show that a hybrid transfer learning model that integrates linguistic features with contextualized language models most faithfully predicts the empirical data. Our work integrates existing approaches to symmetry in natural language and suggests how symmetry inference can improve systematicity in state-of-the-art language models.
