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.- Anthology ID:
- 2020.coling-main.346
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3899–3910
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.346
- DOI:
- 10.18653/v1/2020.coling-main.346
- Cite (ACL):
- Daniele Castellana and Davide Bacciu. 2020. Learning from Non-Binary Constituency Trees via Tensor Decomposition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3899–3910, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Learning from Non-Binary Constituency Trees via Tensor Decomposition (Castellana & Bacciu, COLING 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.coling-main.346.pdf
- Code
- danielecastellana22/tensor-tree-nn
- Data
- SST, SST-2, SST-5