Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
<|reference_start|>Filling Knowledge Gaps in a Broad-Coverage Machine Translation System: 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 Japanese-English MT system.<|reference_end|>
@article{knight1995filling,
    title={Filling Knowledge Gaps in a Broad-Coverage Machine Translation System},
    author={Kevin Knight, Ishwar Chander, Matthew Haines, Vasileios
  Hatzivassiloglou, Eduard Hovy, Masayo Iida, Steve K. Luk, Richard Whitney,
  Kenji Yamada (USC/Information Sciences Institute)},
    journal={arXiv preprint arXiv:cmp-lg/9506009},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9506009},
    primaryClass={cmp-lg cs.CL}
}