Shallow Discourse Parsing Using Distributed Argument Representations and   Bayesian Optimization
  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. 
Akanksha and Jacob Eisenstein
cs.CL