Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

Knowledge-based machine translation (KBMT) techniques yield high quabty in domuoH with detailed semantic models, limited vocabulary, and controlled input grammar Scaling up along these dimensions means acquiring large knowledge resources It also means behaving reasonably when definitive knowledge is not yet available This paper describes how we can fill various KBMT knowledge gap*, often using robust statistical techniques We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT system.

• Publication year

  1995

• Venue

  International Joint Conference on Artificial Intelligence

• Publication date

  1995-06-09

• Fields of study

  Linguistics, Computer Science

• Identifiers
  arXiv cmp-lg/9506009
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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