In this paper we present the first, to the best of our knowledge, discourse parser that is able to predict non-tree DAG structures. We use Integer Linear Programming (ILP) to encode both the objective function and the constraints as global decoding over local scores. Our underlying data come from multi-party chat dialogues, which require the prediction of DAGs. We use the dependency parsing paradigm, as has been done in the past Li et al., 2014; Afantenos et al., 2015) , but we use the underlying formal framework of SDRT and exploit SDRT's notions of left and right distributive relations. We achieve an Fmeasure of 0.531 for fully labeled structures which beats the previous state of the art.
REF ) further used Integer Linear Programming (ILP) to construct a dependency graph.
Integer Linear Programming for Discourse Parsing
HLT-NAACL
Computer Science