  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.
