Enlisting the Ghost: Modeling Empty Categories for Machine Translation
Venue
Proceedings of ACL, ACL (2013), pp. 822-831
Publication Year
2013
Authors
Bing Xiang, Xiaoqiang Luo, Bowen Zhou
BibTeX
Abstract
Empty categories (EC) are artificial elements in Penn Treebanks motivated by the
government-binding (GB) theory to explain certain language phenomena such as
pro-drop. ECs are ubiquitous in languages like Chinese, but they are tacitly
ignored in most machine translation (MT) work because of their elusive nature. In
this paper we present a comprehensive treatment of ECs by first recovering them with
a structured MaxEnt model with a rich set of syntactic and lexical features, and
then incorporating the predicted ECs into a Chinese-to-English machine translation
task through multiple approaches, including the extraction of EC-specific sparse
features. We show that the recovered empty categories not only improve the word
alignment quality, but also lead to significant improvements in a large-scale
state-of-the-art syntactic MT system.
