At its core, the system is powered by two ${\lambda}$-calculi, used as syntactic and semantic theories, respectively. Sentences are first converted to syntactic proofs and terms of the linear ${\lambda}$-calculus using a choice of two parsers: an Alpino-based pipeline, and Neural Proof Nets. The syntactic terms are then converted to semantic terms of the simply typed ${\lambda}$-calculus, via a set of hand designed type- and term-level transformations. Pairs of semantic terms are then fed to an automated theorem prover for natural logic which reasons with them while using lexical relations found in the Open Dutch WordNet. We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset, and achieve promising results, remaining only within a $1.1-3.2{\%}$ performance margin to strong neural baselines. To the best of our knowledge, the reasoning pipeline is the first logic-based system for Dutch.
翻译:其核心是,该系统的动力来自2美元(lambda)-卡库利,分别用作合成学和语义学理论。判决首先转换成合成证据和线性价格(lambda)-卡库卢斯的术语,使用两种剖析器的选择:以阿尔卑诺为主的管道和神经校准网。综合术语随后转换成简单打成美元(lambda)-卡库卢斯的语义术语,通过一套手工设计的型态和术语级转换。语义术语随后被输入到一个自然逻辑的自动理论证明中,这些逻辑在使用荷兰开放的WordNet 中发现的原因。我们评估最近创建的荷兰自然语言的推断数据集的推理管道,并取得有希望的结果,仅停留在11-3.2%的性能幅度以至坚固的神经基线。据我们所知,推理管道是荷兰的第一个基于逻辑的系统。