  Recent work on aspect-level sentiment classification has demonstrated the
efficacy of incorporating syntactic structures such as dependency trees with
graph neural networks(GNN), but these approaches are usually vulnerable to
parsing errors. To better leverage syntactic information in the face of
unavoidable errors, we propose a simple yet effective graph ensemble technique,
GraphMerge, to make use of the predictions from differ-ent parsers. Instead of
assigning one set of model parameters to each dependency tree, we first combine
the dependency relations from different parses before applying GNNs over the
resulting graph. This allows GNN mod-els to be robust to parse errors at no
additional computational cost, and helps avoid overparameterization and
overfitting from GNN layer stacking by introducing more connectivity into the
ensemble graph. Our experiments on the SemEval 2014 Task 4 and ACL 14 Twitter
datasets show that our GraphMerge model not only outperforms models with single
dependency tree, but also beats other ensemble mod-els without adding model
parameters.
