Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
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.
@inproceedings{ijcai1995p179,
title = {Filling Knowledge Gaps in a Broad-Coverage Machine Translation System},
author = {Knight, Kevin and Chander, Ishwar and Haines, Matthew and Hatzivassiloglou, Vasileios and Hovy, Eduard H. and Iida, Masayo and Luk, Steve K. and Whitney, Richard and Yamada, Kenji},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1390--1396},
year = {1995},
note = {Natural Language},
url = {https://ijcai.org/proceedings/1995/179},
}
TY - CPAPER TI - Filling Knowledge Gaps in a Broad-Coverage Machine Translation System AU - Knight, Kevin AU - Chander, Ishwar AU - Haines, Matthew AU - Hatzivassiloglou, Vasileios AU - Hovy, Eduard H. AU - Iida, Masayo AU - Luk, Steve K. AU - Whitney, Richard AU - Yamada, Kenji T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1390 EP - 1396 UR - https://ijcai.org/proceedings/1995/179 ER -Copied
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