  Recent years have witnessed the emerging success of leveraging syntax graphs
for the target sentiment classification task. However, we discover that
existing syntax-based models suffer from two issues: noisy information
aggregation and loss of distant correlations. In this paper, we propose a novel
model termed Neural Subgraph Explorer, which (1) reduces the noisy information
via pruning target-irrelevant nodes on the syntax graph; (2) introduces
beneficial first-order connections between the target and its related words
into the obtained graph. Specifically, we design a multi-hop actions score
estimator to evaluate the value of each word regarding the specific target. The
discrete action sequence is sampled through Gumble-Softmax and then used for
both of the syntax graph and the self-attention graph. To introduce the
first-order connections between the target and its relevant words, the two
pruned graphs are merged. Finally, graph convolution is conducted on the
obtained unified graph to update the hidden states. And this process is stacked
with multiple layers. To our knowledge, this is the first attempt of
target-oriented syntax graph pruning in this task. Experimental results
demonstrate the superiority of our model, which achieves new state-of-the-art
performance.
