Proceedings

Example-Based Machine Translation on Massively Parallel Processors

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Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Natural Language. Pages 1283-1288.

Abstract

This paper describes an Example-Based Machine Translation (EBMT) system implemented on massively parallel computers. EBMT is a novel approach to machine translation that directly uses translation examples to carry out translation tasks . The authors have shown that EBMT attains substantially higher accuracy than traditional machine translation systems. However , EBMT is so computationally demanding that the system would run very slowly on sequential machines and even on a loosely-coupled multi-processor iPSC/2 . We have implemented EBMT on an IXM2 massively parallel associative memory processor and a CM-2 connection machine . Experimental results demonstrate that EBMT on the IXM2 exhibits the best performance and attains aresponse speed that will suffice for real-time tasks such as interpreting telephony.

Eiichiro Sumita, Kozo Oi, Osamu Furuse, Hitoshi Iida, Tetsuya Higuchi, Naoto Takahashi, and Hiroaki Kitano. 1993. Example-Based Machine Translation on Massively Parallel Processors. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93. IJCAI, 1283–1288. https://ijcai.org/proceedings/1993/177
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