Globally Normalized Transition-Based Neural Networks

ACL 2016 Daniel AndorChris AlbertiDavid WeissAliaksei SeverynAlessandro PrestaKuzman GanchevSlav PetrovMichael Collins

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. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Dependency Parsing Penn Treebank Andor et al. POS 97.44 # 2
UAS 94.61 # 5
LAS 92.79 # 7