Filling knowledge gaps in a broad coverage machine translation system

Authors:
Kevin Knight;Ishwar Chander;Matthew Haines;Vasileios Hatzivassiloglou;Eduard Hovy;Masayo Iida;Steve K. Luk;Richard Whitney;Kenji Yamada
Affiliations:
USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA;USC, Information Sciences Institute, Marina del Rey, CA
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Year:
1995

Citing 13
Cited 16

Introduction

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Abstract

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.