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Who did what to whom is a major focus in natural language understanding,which is right the aim of semantic role labeling (SRL) task. Despite of sharinga lot of processing characteristics and even task purpose, it is surprisinglythat jointly considering these two related tasks was never formally reported inprevious work. Thus this paper makes the first attempt to let SRL enhance textcomprehension and inference through specifying verbal predicates and theircorresponding semantic roles. In terms of deep learning models, our embeddingsare enhanced by explicit contextual semantic role labels for more fine-grainedsemantics. We show that the salient labels can be conveniently added toexisting models and significantly improve deep learning models in challengingtext comprehension tasks. Extensive experiments on benchmark machine readingcomprehension and inference datasets verify that the proposed semantic learninghelps our system reach new state-of-the-art over strong baselines which havebeen enhanced by well pretrained language models from the latest progress.

Explicit Contextual Semantics for Text Comprehension