Papers for Relevance Assessment by Robert C. Moore

Research Question: What features should be included in the model?

Reformulation: What features should be included in a weighted linear model? - word alignment

Paper ID
(Link to PDF)

Title

Author(s)

W03-0315

Efficient Optimization for Bilingual Sentence Alignment Based on Linear Regression

Bing Zhao; Klaus Zechner; Stephen Vogel; Alex Waibel

W03-0306

Word Alignment Baselines

John C. Henderson

W05-0612

An Expectation Maximization Approach to Pronoun Resolution

Colin Cherry; Shane Bergsma

54_Paper

A Smorgasbord of Features for Statistical Machine Translation

Franz Josef Och, Daniel Gildea, Sanjeev Khudanpur, Anoop Sarkar, Kenji Yamada, Alex Fraser, Shankar Kumar, Libin Shen, David Smith, Katherine Eng, Viren Jain, Zhen Jin and Dragomir Radev

5-115

Improving Statistical Word Alignment with a Rule-Based Machine Translation System

Hua Wu and Haifeng Wang

W05-0821

Improved Language Modeling for Statistical Machine Translation

Katrin Kirchhoff; Mei Yang

H05-1097

Word-Sense Disambiguation for Machine Translation

David Vickrey; Luke Biewald; Marc Teyssier; Daphne Koller

32-618

Symmetric Word Alignments for Statistical Machine Translation

Evgeny Matusov, Richard Zens and Hermann Ney

P03-1012

A Probability Model to Improve Word Alignment

Colin Cherry; Dekang Lin

C00-2163

A Comparison of Alignment Models for Statistical Machine Translation

Franz Josef Och; Hermann Ney

W05-0908

On Some Pitfalls in Automatic Evaluation and Significance Testing for MT

Stefan Riezler; John T. Maxwell

W93-0301

Robust Bilingual Word Alignment for Machine Aided Translation

Ido Dagan; Kenneth Church; Willian Gale

H05-1010

A Discriminative Matching Approach to Word Alignment

Ben Taskar; Lacoste-Julien Simon; Klein Dan

352_pdf_2-col

Improving IBM Word Alignment Model 1

Robert C. Moore

P05-2012

Phrase Linguistic Classification and Generalization for Improving Statistical Machine Translation

Adria de Gispert