26 research outputs found
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Sequence tagging models for constituent parsing are faster, but less accurate
than other types of parsers. In this work, we address the following weaknesses
of such constituent parsers: (a) high error rates around closing brackets of
long constituents, (b) large label sets, leading to sparsity, and (c) error
propagation arising from greedy decoding. To effectively close brackets, we
train a model that learns to switch between tagging schemes. To reduce
sparsity, we decompose the label set and use multi-task learning to jointly
learn to predict sublabels. Finally, we mitigate issues from greedy decoding
through auxiliary losses and sentence-level fine-tuning with policy gradient.
Combining these techniques, we clearly surpass the performance of sequence
tagging constituent parsers on the English and Chinese Penn Treebanks, and
reduce their parsing time even further. On the SPMRL datasets, we observe even
greater improvements across the board, including a new state of the art on
Basque, Hebrew, Polish and Swedish.Comment: NAACL 2019 (long papers). Contains corrigendu
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
In Semantic Dependency Parsing (SDP), semantic relations form directed
acyclic graphs, rather than trees. We propose a new iterative predicate
selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based
and transition-based parsing approaches in order to handle multiple semantic
head words. We train the IPS model using a combination of multi-task learning
and task-specific policy gradient training. Trained this way, IPS achieves a
new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we
observe that policy gradient training learns an easy-first strategy.Comment: ACL2019 Long accepted. 9 pages for the paper and the additional 2
pages for the supplemental materia