2 research outputs found
A Minimal Span-Based Neural Constituency Parser
In this work, we present a minimal neural model for constituency parsing
based on independent scoring of labels and spans. We show that this model is
not only compatible with classical dynamic programming techniques, but also
admits a novel greedy top-down inference algorithm based on recursive
partitioning of the input. We demonstrate empirically that both prediction
schemes are competitive with recent work, and when combined with basic
extensions to the scoring model are capable of achieving state-of-the-art
single-model performance on the Penn Treebank (91.79 F1) and strong performance
on the French Treebank (82.23 F1).Comment: To appear in ACL 201
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Kuhlmann et al. (2011) to produce the first implementation of
worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition
systems. With our minimal features, we also present O(n^3) global training
methods. Finally, using ensembles including our new parsers, we achieve the
best unlabeled attachment score reported (to our knowledge) on the Chinese
Treebank and the "second-best-in-class" result on the English Penn Treebank.Comment: Proceedings of EMNLP, 2017. 12 page