A simple but effective model for attachment in discourse parsing with multi-task learning for relation labeling

Overview & Synopsis

We present a discourse parsing model for conversation trained on the STAC corpus (Asher et al., 2016). We fine-tune a BERT-based model to encode pairs of discourse units and use a simple linear layer to predict discourse attachments. We then exploit a multi-task setting to predict relation labels, which effectively aids in the difficult task of relation type prediction; our F1-score equals or surpasses the state of the art in the approaches we have reimplemented using code from the authors with no loss in performance for attachment, confirming the intuitive interdependence of these two tasks. Our method also improves over other discourse parsing models in the literature in permitting attachments in which one node has multiple parents, an important feature of multiparty conversation.