58 research outputs found
Learning from Non-Binary Constituency Trees via Tensor Decomposition
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.Comment: Accepted at COLING202
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