End-to-end Graph-based TAG Parsing with Neural Networks
Table 6: Parser accuracy on the unbounded dependency corpus. The results of the first five parsers are taken from Rimell et al. (2009) and Nivre et al. (2010). The Total and Avg columns indicate the percentage of correctly recovered dependencies out of all dependencies and the average of accuracy on the 7 constructions.
['System', 'ObRC', 'ObRed', 'SbRC', 'Free', 'ObQ', 'RNR', 'SbEm', 'Total', 'Avg']
Our joint parser outperforms the other parsers, including the neural network shift-reduce TAG parser Kasai et al. Our data-driven parsers yield relatively low performance in the ObQ and RNR constructions. Performance on ObQ is low, we expect, because of their rarity in the data on which the parser is trained. For RNR, rarity may be an issue as well as the limits of the TAG analysis of this construction. Nonetheless, we see that the rich structural representations that a TAG parser provides enables substantial improvements in the extraction of unbounded dependencies. In the future, we hope to evaluate state-of-the-art Stanford dependency parsers automatically.