Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning
- Publisher:
- International Joint Conferences on Artificial Intelligence
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022, -, (-), pp. 4425-4431
- Issue Date:
- 2022-01-01
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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 2022 International Joint Conferences on Artificial Intelligence All rights reserved
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