Sequence Labeling Parsing by Learning Across Representations
Table 1: Comparison of the single-paradigm models for dependency parsing evaluated on the PTB dev set where each label is learned as single, 2- or 3-tasks.
['Model', 'UAS', 'LAS']
Single-paradigm, multi-task models (s-mtl) For constituency parsing, instead of predicting a single label output of the form (ni,ci,ui), we generate three partial and separate labels ni, ci and ui through three task-dependent feed-forward networks on the top of the stacked bilstms. This is similar to Vilares et al. For dependency parsing, we propose in this work a mtl version too. that casting the problem as 3-task learning led to worse results. Instead, we cast it as a 2-task learning problem, where the first task consists in predicting the head of a word wi, i.e. predicting the tuple (oi,pi), and the second task predicts the type of the relation (di). The loss is here computed as L=∑tLt, where Lt is the partial loss coming from the subtask t.