Shallow Discourse Parsing Using Distributed Argument Representations and Bayesian Optimization

Abstract

This paper describes the Georgia Tech team's approach to the CoNLL-2016 supplementary evaluation on discourse relation sense classification. We use long short-term memories (LSTM) to induce distributed representations of each argument, and then combine these representations with surface features in a neural network. The architecture of the neural network is determined by Bayesian hyperparameter search.

describes our system at the CoNLL 2016 shared task

References in corpus (4)

• Scikit-learn: Machine Learning in Python
• Practical Bayesian Optimization of Machine Learning Algorithms
• When Are Tree Structures Necessary for Deep Learning of Representations?
• One Vector is Not Enough: Entity-Augmented Distributional Semantics for Discourse Relations