Learning from Non-Binary Constituency Trees via Tensor Decomposition
Daniele Castellana, Davide Bacciu · 2020
Processing sentence constituency trees in binarised form is a common and popular approach in literature.However, constituency trees are non-binary by nature.The binarisation procedure changes deeply the structure, furthering constituents that instead are close.In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models.In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure.A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters.Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.