Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data

• URL: http://arxiv.org/abs/2006.10619v2
• Date: Thu, 13 Aug 2020 12:03:25 GMT
• Title: Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data
• Authors: Daniele Castellana and Davide Bacciu
• Abstract summary: We introduce two new aggregation functions to encode structural knowledge from tree-structured data. We test them on two tree classification tasks, showing the advantage of proposed models when tree outdegree increases.
• Score: 12.069862650316262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data. They leverage the Canonical and Tensor-Train decompositions to yield expressive context aggregation while limiting the number of model parameters. Finally, we define two novel neural recursive models for trees leveraging such aggregation functions, and we test them on two tree classification tasks, showing the advantage of proposed models when tree outdegree increases.

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