  Question answering systems usually use keyword searches to retrieve potential
passages related to a question, and then extract the answer from passages with
the machine reading comprehension methods. However, many questions tend to be
unanswerable in the real world. In this case, it is significant and challenging
how the model determines when no answer is supported by the passage and
abstains from answering. Most of the existing systems design a simple
classifier to determine answerability implicitly without explicitly modeling
mutual interaction and relation between the question and passage, leading to
the poor performance for determining the unanswerable questions. To tackle this
problem, we propose a Multi-Step Co-Interactive Relation Network (MCR-Net) to
explicitly model the mutual interaction and locate key clues from coarse to
fine by introducing a co-interactive relation module. The co-interactive
relation module contains a stack of interaction and fusion blocks to
continuously integrate and fuse history-guided and current-query-guided clues
in an explicit way. Experiments on the SQuAD 2.0 and DuReader datasets show
that our model achieves a remarkable improvement, outperforming the BERT-style
baselines in literature. Visualization analysis also verifies the importance of
the mutual interaction between the question and passage.
