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Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data

2020-06-18Code Available0· sign in to hype

Daniele Castellana, Davide Bacciu

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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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