Transition-based discourse parsing with multilayer stack long short term memory

Abstract

Discourse parsing aims to identify the relationship between different discourse units, where most previous works focus on recovering the constituency structure among discourse units with carefully designed features. In this paper, we propose to exploit Long Short Term Memory (LSTM) to properly represent discourse units, while using as few feature engineering as possible. Our transition based parsing model features a multilayer stack LSTM framework to discover the dependency structures among different units. Experiments on RST Discourse Treebank show that our model can outperform traditional feature based systems in terms of dependency structures, without complicated feature design. When evaluated in discourse constituency, our parser can also achieve promising performance compared to the state-of-the-art constituency discourse parsers.