Table of Contents

Keynote Presentations

Can MT really help the Department of Defense?

Nicholas Bemish, Defense Intelligence Agency

The Triple-Advantage Factor of MT: Cost, Time-to-Market, and FAUT

Will Burgett & Julie Chang, Intel Corporation

Bringing humans into the loop: Localization with MT at Traslán

Declan Groves, Traslán Translation Technology

Machine Translation 2008: Science meets Solution

Mark Tapling, Language Weaver

MT Research

Spanish-to-Basque MultiEngine Machine Translation for a Restricted Domain

Iñaki Alegria, Arantza Casillas, Arantza Diaz de Ilarraza, Jon Igartua, Gorka Labaka, Mikel Lersundi, Aingeru Mayor & Kepa Sarasola,
University of the Basque Country

Exploiting Document-Level Context for Data-Driven Machine Translation

Ralf Brown, Carnegie-Mellon

Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models

Mauro Cettolo, Marcello Federico, Daniele Pighin & Nicola Bertoldi, Fondazione Bruno Kessler

Discriminative, Syntactic Language Modeling through Latent SVMs

Colin Cherry & Chris Quirk, Microsoft Research

Translation universals: do they exist? A corpus-based NLP study of convergence and simplification

Gloria Corpas Pastor, Ruslan Mitkov, Naveed Afzal, & Viktor Pekar, University of Málaga/University of Wolverhampton/OUP

Computing multiple weighted reordering hypotheses for a phrase-based statistical machine translation system

Marta R. Costa-jussà & José A. R. Fonollosa,
Universitat Politècnica de Catalunya

Overcoming Vocabulary Sparsity in MT Using Lattices

Steve DeNeefe, Ulf Hermjakob & Kevin Knight,
University of Southern California/ISI

Toward the Evaluation of Machine Translation Using Patent Information

Atsushi Fujii, Masao Utiyama, Mikio Yamamoto & Takehito Utsuro, University of Tsukuba

Automatic Learning of Morphological Variations for Handling Out-of-Vocabulary Terms in Urdu-English MT

Nizar Habash & Hayden Metsky, Columbia University

A Generalized Reordering Model for Phrase-Based Statistical Machine Translation

Yanqing He & Chengqing Zong, Chinese Academy of Sciences

A truly multilingual, high coverage, accurate, yet simple, subsentential alignment method

Adrien Lardilleux & Yves Lepage, Université de Caen

Large-scale Discriminative n-gram Language Models for Statistical Machine Translation

Zhifei Li & Sanjeev Khudanpur,
Nanyang Technological University of Singapore

Are Multiple Reference Translations Necessary? Investigating the Value of Paraphrased Reference Translations in Parameter Optimization

Nitin Madnani, Philip Resnik, Bonnie J. Dorr & Richard Schwartz, University of Maryland

Integrating a Phrase-based SMT Model and a Bilingual Lexicon for Semi-Automatic Acquisition of Technical Term Translation Lexicons

Yohei Morishita, Takehito Utsuro & Mikio Yamamoto,
University of Tsukuba

Linguistically-motivated Tree-based Probabilistic Phrase Alignment

Toshiaki Nakazawa & Sadao Kurohashi,
University of Tokyo/Kyoto University

Parsers as language models for statistical machine translation

Matt Post & Daniel Gildea, University of Rochester

A Statistical Analysis of Automated MT Evaluation Metrics for Assessments in Task-Based MT Evaluation

Calandra R. Tate, Army Research Laboratory

Wider Pipelines: N-Best Alignments and Parses in MT Training

Ashish Venugopal, Andreas Zollmann, Noah A. Smith & Stephan Vogel, Carnegie-Mellon University

Improving English-to-Chinese Translation for Technical Terms using Morphological Information

Xianchao Wu, Naoaki Okazaki, Takashi Tsunakawa & Jun'ichi Tsujii, University of Tokyo

Mining the Web for Domain-Specific Translations

Jian-Cheng Wu, Peter Wei-Huai Hsu, Chiung-Hui Tseng & Jason S. Chang, National Tsing Hua University of Taiwan

Two-Stage Translation: A Combined Linguistic and Statistical Machine Translation Framework

Yushi Xu & Stephanie Seneff, MIT/CSAIL

Student Research Workshop

Improving Syntax-Driven Translation Models by Re-structuring Divergent and Non-isomorphic Parse Tree Structures

Vamshi Ambati & Alon Lavie, Carnegie Mellon/LTI

Using Bilingual Chinese-English Word Alignments to Resolve PP-attachment Ambiguity in English

Victoria Fossum & Kevin Knight,
University of Michigan, Ann Arbor/USC ISI

Combination of Machine Translation Systems via Hypothesis Selection from Combined N-Best Lists

Almut Silja Hildebrand & Stephan Vogel,
Carnegie Mellon/InterACT

The Value of Machine Translation for the Professional Translator

Elina Lagoudaki, Imperial College London

Diacritization as a Machine Translation and Sequence Labeling Problem

Tim Schlippe, ThuyLinh Nguyen & Stephan Vogel,
Carnegie Mellon/InterACT

Multi-Source Translation Methods

Lane Schwartz, University of Minnesota

Government & Commercial Uses of MT

Machine Translation for Triage and Exploitation of Massive Text Data

James E. Andrews & Kristen Summers,
National Ground Intelligence Center

Global Public Health Intelligence Network (GPHIN)

Michael Blench, Public Health Agency of Canada

Sharing User Dictionaries Across Multiple Systems with UTX-S

Francis Bond, Seiji Okura, Yuji Yamamoto, Toshiki Murata, Kiyotaka Uchimoto, Michael Kato, Miwako Shimazu & Tsugiyoshi Suzuki, National Institute of Information and Communications Technology, et al.

Many-to-Many Multilingual Medical Speech Translation on a PDA

Pierrette Bouillon, Glenn Flores, Maria Georgescul, Sonia Halimi, Beth Ann Hockey, Hitoshi Isahara, Kyoko Kanzaki, Yukie Nakao, Manny Rayner, Marianne Santaholma, Marianne Starlander & Nikos Tsourakis,
University of Geneva/ISSCO, et al.

MT errors in Chinese-to-English MT systems

Shin Chang-Meadows, CTWG

Working with the US Government: Information Resources

Jennifer DeCamp, MITRE

The Language Technology Resource Center

Jennifer DeCamp, MITRE

Reliable Innovation: A Tecchie's Travels in the Land of Translators

Alain Désilets, Louise Brunette, Christiane Melançon, & Geneviève Patenaude, National Research Council of Canada/Université du Québec en Outaouais

Automated Machine Translation Improvement Through Post-Editing Techniques: Analyst and Translator Experiments

Jennifer Doyon, Christine Doran, C. Donald Means, & Domenique Parr,
Abraxas Corp / MITRE

User-centered MT Development and Implementation

Kathleen Egan, Francis Kubala & Allen Sears, TSWG