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Fast and Extensible Phrase Scoring for Statistical Machine Translation

  • Fondazione Bruno Kessler
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-review

Open access

Publication Information

Output type

Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-review

Original language

English

Journal (Volume, Issue Number)

Prague Bulletin of Mathematical Linguistics

Publication milestones

  • Published - 26/02/2010

Publication status

Published - 26/02/2010

Publication IDs

  • ORCID: /0000-0002-6103-7275/work/106363214

Abstract

Existing tools for generating phrase tables for phrase-based Statistical Machine Translation (SMT) are generally optimised towards low memory use to allow processing of large corpora with limited memory. Whilst being a reasonable design choice, this approach does not make optimal use of resources when the sufficient memory is available. We present memscore, a new open-source tool to score phrases in memory. Besides acting as a faster drop-in replacement for existing software, it implements a number of standard smoothing techniques and provides a platform for easy experimentation with new scoring methods.