The dependency parser and POS tagger are trained on supervised data and up-trained on data labeled by the CKY-style bottom-up constituent parser of , a state of the art broadcast news (BN) parser, with phrase structures converted to labeled dependencies by the Stanford converter.
while accurate, the parser has a huge grammar (32GB) from using products of latent variable grammars and requires O(l 3 ) time to parse a sentence of length l. we could not use the constituent parser for ASR rescoring since utterances can be very long, although the shorter up-training text data was not a problem.