Proceedings

Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

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Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Natural Language. Pages 1390-1396.

Abstract

Knowledge-based machine translation (KBMT) techniques yield high quality in domains 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 gaps, often using robust statistical techniques. We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage JapaneseEnglish MT system.

Kevin Knight, Ishwar Chander, Matthew Haines, Vasileios Hatzivassiloglou, Eduard H. Hovy, Masayo Iida, Steve K. Luk, Richard Whitney, and Kenji Yamada. 1995. Filling Knowledge Gaps in a Broad-Coverage Machine Translation System. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95. IJCAI, 1390–1396. https://ijcai.org/proceedings/1995/179
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