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Multitask Pointer Network for Multi-Representational Parsing
- Publication Year :
- 2020
-
Abstract
- We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.<br />Comment: Final peer-reviewed manuscript accepted for publication in Knowledge-Based Systems
- Subjects :
- Computer Science - Computation and Language
68T50
I.2.7
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2009.09730
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.knosys.2021.107760