论文标题
企鹅不飞：通过实例化和例外，关于仿制药的推理
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
论文作者
论文摘要
仿制药对世界（例如，鸟类可以飞来飞去）的概括（例如，新生儿鸟和企鹅不能飞行）。常识知识基础在NLP中广泛使用，编码了一些通用知识，但很少列举这种例外，并且知道通用陈述何时成立或不正确，对于建立对仿制药的全面理解至关重要。我们提出了一个新颖的框架，该框架是通过语言理论告知的，以产生示例 - 当通用性具有真实或错误时。我们为〜650的仿制药生成了〜19K的示例，并表明我们的框架的表现优于强大的GPT-3基线，高于12.8的精度点。我们的分析强调了基于语言理论的可控性在产生示例的重要性，知识库作为示例来源的不足以及对自然语言推断任务的挑战所构成的挑战。
Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, used extensively in NLP, encode some generic knowledge but rarely enumerate such exceptions and knowing when a generic statement holds or does not hold true is crucial for developing a comprehensive understanding of generics. We present a novel framework informed by linguistic theory to generate exemplars -- specific cases when a generic holds true or false. We generate ~19k exemplars for ~650 generics and show that our framework outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplars, and the challenges exemplars pose for the task of natural language inference.
