Efficient Top-Down BTG Parsing for Machine Translation Preordering
Venue
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics (2015), pp. 208-218
Publication Year
2015
Authors
Tetsuji Nakagawa
BibTeX
Abstract
We present an efficient incremental top-down parsing method for preordering based
on Bracketing Transduction Grammar (BTG). The BTG-based preordering framework
(Neubig et al., 2012) can be applied to any language using only parallel text, but
has the problem of computational efficiency. Our top-down parsing algorithm allows
us to use the early update technique easily for the latent variable structured
Perceptron algorithm with beam search, and solves the problem. Experimental results
showed that the top-down method is more than 10 times faster than a method using
the CYK algorithm. A phrase-based machine translation system with the top-down
method had statistically significantly higher BLEU scores for 7 language pairs
without relying on supervised syntactic parsers, compared to baseline systems using
existing preordering methods.
