The preservation of meaning between inputs and outputs is perhaps

the most ambitious and, often, the most elusive goal of systems

that attempt to process natural language. Nowhere is this goal of

more obvious importance than for the tasks of machine translation

and paraphrase generation. Preserving meaning between the input and

the output is paramount for both, the monolingual vs bilingual distinction

notwithstanding. In this thesis, I present a novel, symbiotic relationship

between these two tasks that I term the "circle of meaning''.

Today's statistical machine translation (SMT) systems require high

quality human translations for parameter tuning, in addition to

large bi-texts for learning the translation units. This parameter

tuning usually involves generating translations at different points

in the parameter space and obtaining feedback against human-authored

reference translations as to how good the translations. This feedback

then dictates what point in the parameter space should be explored

next. To measure this feedback, it is generally considered wise to have

multiple (usually 4) reference translations to avoid unfair penalization of translation

hypotheses which could easily happen given the large number of ways in which

a sentence can be translated from one language to another. However, this reliance on multiple reference translations

creates a problem since they are labor intensive and expensive to obtain.

Therefore, most current MT datasets only contain a single reference.

This leads to the problem of reference sparsity---the primary open problem

that I address in this dissertation---one that has a serious effect on the

SMT parameter tuning process.

Bannard and Callison-Burch (2005) were the first to provide a practical

connection between phrase-based statistical machine translation and paraphrase

generation. However, their technique is restricted to generating phrasal

paraphrases. I build upon their approach and augment a phrasal paraphrase

extractor into a sentential paraphraser with extremely broad coverage.

The novelty in this augmentation lies in the further strengthening of

the connection between statistical machine translation and paraphrase

generation; whereas Bannard and Callison-Burch only relied on SMT machinery

to extract phrasal paraphrase rules and stopped there, I take it a few

steps further and build a full English-to-English SMT system. This system

can, as expected, ``translate'' any English input sentence into a new English

sentence with the same degree of meaning preservation that exists in a bilingual

SMT system. In fact, being a state-of-the-art SMT system, it is able to generate

n-best "translations" for any given input sentence. This sentential

paraphraser, built almost entirely from existing SMT machinery, represents

the first 180 degrees of the circle of meaning.

To complete the circle, I describe a novel connection in the other direction.

I claim that the sentential paraphraser, once built in this fashion, can

provide a solution to the reference sparsity problem and, hence, be used

to improve the performance a bilingual SMT system. I discuss two different

instantiations of the sentential paraphraser and show several results that

provide empirical validation for this connection.