Embedding Lexical Features via Low-Rank Tensors
Table 4: PP-attachment test accuracy. The baseline results are from Belinkov et al. (2014).
['[BOLD] System', '[BOLD] Resources Used', '[BOLD] Acc']
\newcitebelinkov2014exploring also reported results of parsers and parser re-rankers, which can access to additional resources (complete parses for training and complete sentences as inputs) so it is unfair to compare them with the standalone systems like HPCD and our lrfr. Nonetheless lrfr1-tucker & lrfr2-cp (90.3) still outperforms the state-of-the-art parser RBG (88.4), re-ranker Charniak-RS (88.6), and the combination of the state-of-the-art parser and compositional model RBG + HPCD (90.1). Thus, even with fewer resources, lrfr becomes the new best system.