End-to-end Graph-based TAG Parsing with Neural Networks
Table 5: PETE test results. Precision (P), recall (R), and F1 are calculated for “entails.”
['System', '%A', '%P', '%R', 'F1']
One configuration is a pipeline approach that runs our BiLSTM POS tagger, supertagger, and parser. The other one is a joint approach that only uses our full joint parser. The joint method yields 78.1% in accuracy and 76.4% in F1, improvements of 2.4 and 2.7 points over the previously reported best results.