40 citations · 109 across the 3 of their papers we have counts for
6 papers
Natural Language Processing with Small Feed-Forward Networks
Jan A. Botha, Emily Pitler, Ji Ma +5
We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while bein…
SyntaxNet Models for the CoNLL 2017 Shared Task
Chris Alberti, Daniel Andor, Ivan Bogatyy +10
We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call "ParseySaurus," uses the DRAGNN framework [Kong et al, 2017] to combine t…
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
Lingpeng Kong, Chris Alberti, Daniel Andor +2
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent…
Stack-propagation: Improved Representation Learning for Syntax
Yuan Zhang, David Weiss
Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utiliz…
Globally Normalized Transition-Based Neural Networks
Daniel Andor, Chris Alberti, David Weiss +5
We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results.…
Structured Training for Neural Network Transition-Based Parsing
David Weiss, Chris Alberti, Michael Collins +1
We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a larg…