@inproceedings{castellana-bacciu-2020-learning,
title = "Learning from Non-Binary Constituency Trees via Tensor Decomposition",
author = "Castellana, Daniele and
Bacciu, Davide",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.346",
doi = "10.18653/v1/2020.coling-main.346",
pages = "3899--3910",
abstract = "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.",
}
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%0 Conference Proceedings
%T Learning from Non-Binary Constituency Trees via Tensor Decomposition
%A Castellana, Daniele
%A Bacciu, Davide
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F castellana-bacciu-2020-learning
%X 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.
%R 10.18653/v1/2020.coling-main.346
%U https://aclanthology.org/2020.coling-main.346
%U https://doi.org/10.18653/v1/2020.coling-main.346
%P 3899-3910
Markdown (Informal)
[Learning from Non-Binary Constituency Trees via Tensor Decomposition](https://aclanthology.org/2020.coling-main.346) (Castellana & Bacciu, COLING 2020)
ACL