Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations

• URL: http://arxiv.org/abs/2011.13354v4
• Date: Sun, 5 Dec 2021 02:34:30 GMT
• Title: Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations
• Authors: Aditya Kalyanpur, Tom Breloff, David Ferrucci
• Abstract summary: Braid is a novel logical reasoner that supports probabilistic rules. We describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework. We evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results.
• Score: 0.9023847175654603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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