2,459 research outputs found
Dependency Parsing with Dilated Iterated Graph CNNs
Dependency parses are an effective way to inject linguistic knowledge into
many downstream tasks, and many practitioners wish to efficiently parse
sentences at scale. Recent advances in GPU hardware have enabled neural
networks to achieve significant gains over the previous best models, these
models still fail to leverage GPUs' capability for massive parallelism due to
their requirement of sequential processing of the sentence. In response, we
propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for
graph-based dependency parsing, a graph convolutional architecture that allows
for efficient end-to-end GPU parsing. In experiments on the English Penn
TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best
neural network parsers.Comment: 2nd Workshop on Structured Prediction for Natural Language Processing
(at EMNLP '17
Universal Dependencies Parsing for Colloquial Singaporean English
Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.Comment: Accepted by ACL 201
Hierarchical Pointer Net Parsing
Transition-based top-down parsing with pointer networks has achieved
state-of-the-art results in multiple parsing tasks, while having a linear time
complexity. However, the decoder of these parsers has a sequential structure,
which does not yield the most appropriate inductive bias for deriving tree
structures. In this paper, we propose hierarchical pointer network parsers, and
apply them to dependency and sentence-level discourse parsing tasks. Our
results on standard benchmark datasets demonstrate the effectiveness of our
approach, outperforming existing methods and setting a new state-of-the-art.Comment: Accepted by EMNLP 201
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