  Multi-hop machine reading comprehension is a challenging task in natural
language processing as it requires more reasoning ability across multiple
documents. Spectral models based on graph convolutional networks have shown
good inferring abilities and lead to competitive results. However, the analysis
and reasoning of some are inconsistent with those of humans. Inspired by the
concept of grandmother cells in cognitive neuroscience, we propose a
heterogeneous graph attention network model named ClueReader to imitate the
grandmother cell concept. The model is designed to assemble the semantic
features in multi-level representations and automatically concentrate or
alleviate information for reasoning through the attention mechanism. The name
ClueReader is a metaphor for the pattern of the model: it regards the subjects
of queries as the starting points of clues, takes the reasoning entities as
bridge points, considers the latent candidate entities as grandmother cells,
and the clues end up in candidate entities. The proposed model enables the
visualization of the reasoning graph, making it possible to analyze the
importance of edges connecting entities and the selectivity in the mention and
candidate nodes, which is easier to comprehend empirically. Evaluations on the
open-domain multi-hop reading dataset WikiHop and drug-drug interaction dataset
MedHop proved the validity of ClueReader and showed the feasibility of its
application of the model in the molecular biology domain.
