Syntax-Guided Sequence to Sequence Modeling for Discourse Segmentation

Previous studies on RST-style discourse segmentation have achieved impressive results. However, recent neural works either require a complex joint training process or heavily rely on powerful pre-trained word vectors. Under this condition, a simpler but more robust segmentation method is needed. In this work, we take a deeper look into intra-sentence dependencies to investigate if the syntax information is totally useless, or to what extent it can help improve the discourse segmentation performance. To achieve this, we propose a sequence-to-sequence model along with a GCN based encoder to well utilize intra-sentence dependencies and a multi-head biaffine attention based decoder to predict EDU boundaries. Experimental results on two benchmark corpora show that the syntax information we use is significantly useful and the resulting model is competitive when compared with the state-of-the-art.