Computer Science Bldg. Room 601
In broad sense, latent information in natural language processing
tasks refers to any information that is not plainly observable from
raw data. Such latent information is found in abundance in many
natural language processing tasks. Learning latent information itself
could be the purpose of the task or it can be learned and utilized to
improve relevant tasks. For example, in unsupervised learning of word
alignment from parallel corpora, learning latent information is the
task. Learning latent annotation for context free grammar falls into
the latter category since latent annotation leads to better parsing
accuracy. Depending on the availability of the data, latent
information may be learned in a supervised manner or unsupervised
manner.
This dissertation presents three different types of latent information
that are learned and used to improve various natural language
processing tasks, mainly focusing on different stages of machine
translation. First, we discuss unsupervised learning of tokenization
from parallel corpora using alignment between a bilingual sentence
pair as latent information. Second, we examine using empty categories
to improve parsing and machine translation. In these tasks, empty
categories are latent information that are learned from raw text and
applied to the respective tasks. Finally, we look at learning latent
annotation for synchronous context free grammar, which leads us to
more accurate and faster string-to-tree machine translation.