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SYNTACTIC ALIGNMENT MODELS FOR LARGE-SCALE
STATISTICAL MACHINE TRANSLATION
by
Jason A. Riesa
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2012
Copyright 2012 Jason A. Riesa
Object Description
| Title | Syntactic alignment models for large-scale statistical machine translation |
| Author | Riesa, Jason A. |
| Author email | riesa@usc.edu;jason.riesa@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-03-21 |
| Date submitted | 2012-04-19 |
| Date approved | 2012-04-19 |
| Restricted until | 2012-04-19 |
| Date published | 2012-04-19 |
| Advisor (committee chair) | Marcu, Daniel |
| Advisor (committee member) |
Hovy, Eduard Knight, Kevin Narayanan, Shrikanth S. Schaal, Stefan |
| Abstract | Word alignment, the process of inferring the implicit links between words across two languages, serves as an integral piece of the puzzle of learning linguistic translation knowledge. It enables us to acquire automatically from data the rules that govern the transformation of words, phrases, and syntactic structures from one language to another. Word alignment is used in many tasks in Natural Language Processing, such as bilingual dictionary induction, cross-lingual information retrieval, and distilling parallel text from within noisy data. In this dissertation, we focus on word alignment for statistical machine translation. ❧ We advance the state-of-the-art in search, modeling, and learning of alignments and show empirically that, when taken together, these contributions significantly improve the output quality of large-scale statistical machine translation, outperforming existing methods. We show results for Arabic-English and Chinese-English translation. ❧ Ultimately, the work we describe herein may be used for any language-pair, supporting arbitrary and overlapping features from varied sources. Finally, our features are learned automatically without any human intervention, facilitating rapid deployment for new language-pairs. |
| Keyword | translation; machine translation; alignment; word alignment; natural language processing; syntax; machine learning; noisy data |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Riesa, Jason A. |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume1/etd-RiesaJason-621.pdf |
Description
| Title | Page 1 |
| Full text | SYNTACTIC ALIGNMENT MODELS FOR LARGE-SCALE STATISTICAL MACHINE TRANSLATION by Jason A. Riesa A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) May 2012 Copyright 2012 Jason A. Riesa |
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