Unsupervised tokenization for machine translation

  • Authors:
  • Tagyoung Chung;Daniel Gildea

  • Affiliations:
  • University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
  • Year:
  • 2009

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Abstract

Training a statistical machine translation starts with tokenizing a parallel corpus. Some languages such as Chinese do not incorporate spacing in their writing system, which creates a challenge for tokenization. Moreover, morphologically rich languages such as Korean present an even bigger challenge, since optimal token boundaries for machine translation in these languages are often unclear. Both rule-based solutions and statistical solutions are currently used. In this paper, we present unsupervised methods to solve tokenization problem. Our methods incorporate information available from parallel corpus to determine a good tokenization for machine translation.