11,563 research outputs found
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
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
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