Abstract
In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.- Anthology ID:
- Q18-1005
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 6
- Month:
- Year:
- 2018
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova, Brian Roark
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 63–75
- Language:
- URL:
- https://aclanthology.org/Q18-1005
- DOI:
- 10.1162/tacl_a_00005
- Cite (ACL):
- Yang Liu and Mirella Lapata. 2018. Learning Structured Text Representations. Transactions of the Association for Computational Linguistics, 6:63–75.
- Cite (Informal):
- Learning Structured Text Representations (Liu & Lapata, TACL 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/Q18-1005.pdf
- Code
- nlpyang/structured + additional community code
- Data
- SNLI