A customizable, self-learning parameterized MT system: the next generation

Keh-Yih Su, Jing-Shin Chang


Abstract
In this paper, the major problems of the current machine translation systems are first outlined. A new direction, highlighting the system capability to be customizable and self-learnable, is then proposed for attacking the described problems, which are mainly resulted from the very complicated characteristics of natural languages. The proposed solution adopts an unsupervised two-way training mechanism and a parameterized architecture to acquire the required statistical knowledge, such that the system can be easily adapted to different domains and various preferences of individual users.
Anthology ID:
1999.mtsummit-1.29
Volume:
Proceedings of Machine Translation Summit VII
Month:
September 13-17
Year:
1999
Address:
Singapore, Singapore
Venue:
MTSummit
SIG:
Publisher:
Note:
Pages:
182–190
Language:
URL:
https://aclanthology.org/1999.mtsummit-1.29
DOI:
Bibkey:
Cite (ACL):
Keh-Yih Su and Jing-Shin Chang. 1999. A customizable, self-learning parameterized MT system: the next generation. In Proceedings of Machine Translation Summit VII, pages 182–190, Singapore, Singapore.
Cite (Informal):
A customizable, self-learning parameterized MT system: the next generation (Su & Chang, MTSummit 1999)
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PDF:
https://preview.aclanthology.org/update-css-js/1999.mtsummit-1.29.pdf