Experiments and prospects of Example-Based Machine Translation

  • Authors:
  • Eiichiro Sumita;Hitoshi Iida

  • Affiliations:
  • ATR Interpreting Telephony Research Laboratories, Sanpeidani, Inuidani, Seika-cho, Souraku-gun, Kyoto, Japan;ATR Interpreting Telephony Research Laboratories, Sanpeidani, Inuidani, Seika-cho Souraku-gun, Kyoto, Japan

  • Venue:
  • ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
  • Year:
  • 1991

Quantified Score

Hi-index 0.00

Visualization

Abstract

EBMT (Example-Based Machine Translation) is proposed. EBMT retrieves similar examples (pairs of source phrases, sentences, or texts and their translations) from a database of examples, adapting the examples to translate a new input. EBMT has the following features: (1) It is easily upgraded simply by inputting appropriate examples to the database; (2) It assigns a reliability factor to the translation result; (3) It is accelerated effectively by both indexing and parallel computing; (4) It is robust because of best-match reasoning; and (5) It well utilizes translator expertise. A prototype system has been implemented to deal with a difficult translation problem for conventional Rule-Based Machine Translation (RBMT), i.e., translating Japanese noun phrases of the form "N1 no N2" into English. The system has achieved about a 78% success rate on average. This paper explains the basic idea of EBMT, illustrates the experiment in detail, explains the broad applicability of EBMT to several difficult translation problems for RBMT and discusses the advantages of integrating EBMT with RBMT.