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.

本文的研究目的是让语义角色标注（SRL）任务通过指定动词谓词和其相应的语义角色来增强文本理解和推理，并针对深度学习模型，加入语义角色标注以获得更细粒度的语义。经过广泛的实验证明，所提出的语义学习能够在各项挑战性的自然语言理解任务中显著提高现有模型的性能，包括已经用于最新进展的预训练语言模型。