323 research outputs found
A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing
Restricted non-monotonicity has been shown beneficial for the projective
arc-eager dependency parser in previous research, as posterior decisions can
repair mistakes made in previous states due to the lack of information. In this
paper, we propose a novel, fully non-monotonic transition system based on the
non-projective Covington algorithm. As a non-monotonic system requires
exploration of erroneous actions during the training process, we develop
several non-monotonic variants of the recently defined dynamic oracle for the
Covington parser, based on tight approximations of the loss. Experiments on
datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic
dynamic oracle outperforms the monotonic version in the majority of languages.Comment: 11 pages. Accepted for publication at ACL 201
Arc-swift: A Novel Transition System for Dependency Parsing
Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enables direct attachments between tokens farther apart with a single
transition. This allows the parser to leverage lexical information more
directly in transition decisions. Hence, arc-swift can achieve significantly
better performance with a very small beam size. Our parsers reduce error by
3.7--7.6% relative to those using existing transition systems on the Penn
Treebank dependency parsing task and English Universal Dependencies.Comment: Accepted at ACL 201
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