论文标题
结合事件语义和自然语言推理的学位语义
Combining Event Semantics and Degree Semantics for Natural Language Inference
论文作者
论文摘要
在正式的语义中，有两个发达的语义框架：事件语义，它使用事件概念和学位语义来处理动词和副词修饰符，并使用学位概念来分析形容词和比较。但是，是否可以将这些框架合并为处理所讨论现象相互作用的案例并不明显。在这里，我们通过关注自然语言推论（NLI）来研究这个问题。我们实施了基于逻辑的NLI系统，该系统将事件语义和学位语义及其与词汇知识的互动相结合。我们在包含语言上具有挑战性问题的各种NLI数据集上评估了系统。结果表明，与以前的基于逻辑的系统和基于深度学习的系统相比，该系统在这些数据集上达到了高度精确度。这表明可以将两个语义框架始终如一地组合起来，以处理语言现象的各种组合，而不会损害这两个框架的优势。
In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is not obvious whether these frameworks can be combined to handle cases in which the phenomena in question are interacting with each other. Here, we study this issue by focusing on natural language inference (NLI). We implement a logic-based NLI system that combines event semantics and degree semantics and their interaction with lexical knowledge. We evaluate the system on various NLI datasets containing linguistically challenging problems. The results show that the system achieves high accuracies on these datasets in comparison with previous logic-based systems and deep-learning-based systems. This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of either framework.
