morphogen: Translation into Morphologically Rich Languages with Synthetic Phrases

Abstract

We present morphogen, a tool for improving translation into morphologically rich languages with synthetic phrases. We approach the problem of translating into morphologically rich languages in two phases. First, an inflection model is learned to predict target word inflections from source side context. Then this model is used to create additional sentence specific translation phrases. These "synthetic phrases" augment the standard translation grammars and decoding proceeds normally with a standard translation model. We present an open source Python implementation of our method, as well as a method of obtaining an unsupervised morphological analysis of the target language when no supervised analyzer is available.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2013
Accession Number
ADA598334

Entities

People

  • Chris Dyer
  • Eva Schlinger
  • Victor Chahuneau

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Computational Linguistics
  • Computational Science
  • Computer Vision
  • Decoding
  • Feature Extraction
  • Grammars
  • Language
  • Linguistics
  • Machine Learning
  • Machine Translation
  • Models
  • Natural Language Processing
  • Probabilistic Models
  • Probability
  • Sequences
  • Standards

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computational Modeling and Simulation