Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing
Marie Candito
The biaffine parser of Dozat and Manning (2017) was successfully extended to
semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on
graphs is surprisingly high given that, without the constraint of producing a
tree, all arcs for a given sentence are predicted independently from each other
(modulo a shared representation of tokens). To circumvent such an independence
of decision, while retaining the O(n^2) complexity and highly parallelizable
architecture, we propose to use simple auxiliary tasks that introduce some form
of interdependence between arcs. Experiments on the three English acyclic
datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep
syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic
performance gains on a near state-of-the-art baseline using transformer-based
contextualized representations. This provides a simple and robust method to
boost SDP performance.