40 citations · 131 across the 4 of their papers we have counts for
3 papers · 1 filter
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…