论文标题
辫子：将象征和神经知识编织成连贯的逻辑解释
Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations
论文作者
论文摘要
传统的符号推理引擎虽然具有精确和明确性的吸引力，但具有一些主要的缺点：使用依赖逻辑术语的确切匹配（统一）的脆性推理程序，无法处理不确定性，以及需要预先编译的知识规则基础（“知识获取”问题）。为了解决这些问题，我们设计了一个名为Braid的新颖逻辑推理器，该原因支持概率规则，并使用自定义统一功能和动态规则生成的概念来克服传统推理中普遍存在的脆弱匹配和知识差距问题。在本文中，我们描述了编织中使用的推理算法及其在基于任务的框架中的实现，该框架为输入查询构建了证明/说明图。我们使用一个简单的质量检查示例，从儿童的故事中激励Braid的设计，并解释各种组件如何共同起作用以产生连贯的逻辑解释。最后，我们评估了ROC Story Cloze测试的辫子，并在提供基于框架的解释的同时，取得了接近最新的结果。
Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children's story to motivate Braid's design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.
