PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers

Syntactic parsing technologies have become significantly more robust thanks to advancements in their underlying statistical and Deep Neural Network (DNN) techniques: most modern syntactic parsers can produce a syntactic parse tree for almost any sentence, including ones that may not be strictly grammatical. Despite improved robustness, such parsers still do not reflect the alternatives in parsing that are intrinsic in syntactic ambiguities. Two most notable such ambiguities are prepositional phrase (PP) attachment ambiguities and pronoun coreference ambiguities. In this paper, we discuss PatchComm, which uses commonsense knowledge to help resolve both kinds of ambiguities. To the best of our knowledge, we are the first to propose the general-purpose approach of using external commonsense knowledge bases to guide syntactic parsers. We evaluated PatchComm against the state-of-the-art (SOTA) spaCy parser on a PP attachment task and against the SOTA NeuralCoref module on a coreference task. Results show that PatchComm is successful at detecting syntactic ambiguities and using commonsense knowledge to help resolve them.