A Transition-based Algorithm for Unrestricted AMR Parsing
Table 3: Tc scores on the development set.
['[BOLD] Action', '[BOLD] Prec.', '[BOLD] Rec.', '[BOLD] F-score']
confirm and reduce are the easiest transitions, as local information such as POS-tags and words are discriminative to distinguish between content and function words. breakdown is the hardest action. In early stages of this work, we observed that this transition could learn to correctly generate multiple-term concepts for named-entities that are not sparse (e.g. countries or people), but failed with sparse entities (e.g. dates or percent quantities). Low performance on identifying them negatively affects the edge metrics, which require both concepts of an edge to be correct. Because of this and to identify them properly, we use the mentioned complementary rules to handle named entities. right-arcs are harder than left-arcs, although the reason for this issue remains as an open question for us. The performance for no-arcs is high, but it would be interesting to achieve a higher recall at a cost of a lower precision, as predicting no-arcs makes the transition sequence longer, but could help identify more distant reentrancy. The accuracy of Tc is ∼86%. The accuracy of Rc is ∼79%. We do not show the detailed results since the number of classes is too high. Cc was trained on concepts occurring more than 1 time in the training set, obtaining an accuracy of ∼83%. The accuracy on the development set with all concepts was ∼77%.