  For natural language understanding tasks, either machine reading
comprehension or natural language inference, both semantics-aware and inference
are favorable features of the concerned modeling for better understanding
performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to
meet such a motivation. Taking explicit contextualized semantics as a
complementary input, the inferential module of SAIN enables a series of
reasoning steps over semantic clues through an attention mechanism. By
stringing these steps, the inferential network effectively learns to perform
iterative reasoning which incorporates both explicit semantics and
contextualized representations. In terms of well pre-trained language models as
front-end encoder, our model achieves significant improvement on 11 tasks
including machine reading comprehension and natural language inference.
