  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 sharing
a lot of processing characteristics and even task purpose, it is surprisingly
that jointly considering these two related tasks was never formally reported in
previous work. Thus this paper makes the first attempt to let SRL enhance text
comprehension and inference through specifying verbal predicates and their
corresponding semantic roles. In terms of deep learning models, our embeddings
are enhanced by explicit contextual semantic role labels for more fine-grained
semantics. We show that the salient labels can be conveniently added to
existing models and significantly improve deep learning models in challenging
text comprehension tasks. Extensive experiments on benchmark machine reading
comprehension and inference datasets verify that the proposed semantic learning
helps our system reach new state-of-the-art over strong baselines which have
been enhanced by well pretrained language models from the latest progress.
