Transition-based Semantic Dependency Parsing with Pointer Networks
Table 3: Accuracy comparison of state-of-the-art SDP parsers on the SemEval 2015 Task 18 datasets. Gb and Tb stand for graph- and transition-based models, +char and +lemma for augmentations with character-level and lemma embeddings, +Flair and +BERTBASE|LARGE for augmentations with deep contextualized character-level and word-level embeddings, and, finally, +MT, +RL and +Ens for the application of multi-task, reinforcement learning and ensemble techniques.
['Parser', 'DM ID', 'DM OOD', 'PAS ID', 'PAS OOD', 'PSD ID', 'PSD OOD', 'Avg ID', 'Avg OOD']
To perform a fair comparison, we group SDP systems in three blocks dependending on the embeddings provided to the architecture: (1) just basic pre-trained word and POS tag embeddings, (2) character and pre-trained lemma embeddings augmentation and (3) pre-trained deep contextualized embeddings augmentation.