Annotation-Inspired Implicit Discourse Relation Classification with
  Auxiliary Discourse Connective Generation
Wei Liu Michael Strube
Implicit discourse relation classification is a challenging task due to the
absence of discourse connectives. To overcome this issue, we design an
end-to-end neural model to explicitly generate discourse connectives for the
task, inspired by the annotation process of PDTB. Specifically, our model
jointly learns to generate discourse connectives between arguments and predict
discourse relations based on the arguments and the generated connectives. To
prevent our relation classifier from being misled by poor connectives generated
at the early stage of training while alleviating the discrepancy between
training and inference, we adopt Scheduled Sampling to the joint learning. We
evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC. Results
show that our joint model significantly outperforms various baselines on three
datasets, demonstrating its superiority for the task.