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    <abstract>The Burrows-Wheeler Transform (BWT) was originally developed for data compression, but can also be applied to indexing text. In this paper, an adaptation of the BWT to word-based indexing of the training corpus for an example-based machine translation (EBMT) system is presented. The adapted BWT embeds the necessary information to retrieve matched training instances without requiring any additional space and can be instantiated in a compressed form which reduces disk space and memory requirements by about 40% while still remaining searchable without decompression. Both the speed advantage from O(log N) lookups compared to the O(N) lookups in the inverted-file index which had previously been used and the structure of the index itself act as enablers for additional capabilities and run-time speed. Because the BWT groups all instances of any n-gram together, it can be used to quickly enumerate the most-frequent n-grams, for which translations can be precomputed and stored, resulting in an order-of-magnitude speedup at run time.</abstract>
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%0 Conference Proceedings
%T A modified Burrows-Wheeler transform for highly scalable example-based translation
%A Brown, Ralf D.
%Y Frederking, Robert E.
%Y Taylor, Kathryn B.
%S Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2004
%8 sep 28  oct 2
%I Springer
%C Washington, USA
%F brown-2004-modified
%X The Burrows-Wheeler Transform (BWT) was originally developed for data compression, but can also be applied to indexing text. In this paper, an adaptation of the BWT to word-based indexing of the training corpus for an example-based machine translation (EBMT) system is presented. The adapted BWT embeds the necessary information to retrieve matched training instances without requiring any additional space and can be instantiated in a compressed form which reduces disk space and memory requirements by about 40% while still remaining searchable without decompression. Both the speed advantage from O(log N) lookups compared to the O(N) lookups in the inverted-file index which had previously been used and the structure of the index itself act as enablers for additional capabilities and run-time speed. Because the BWT groups all instances of any n-gram together, it can be used to quickly enumerate the most-frequent n-grams, for which translations can be precomputed and stored, resulting in an order-of-magnitude speedup at run time.
%U https://link.springer.com/chapter/10.1007/978-3-540-30194-3_4
%P 27-36

Markdown (Informal)

[A modified Burrows-Wheeler transform for highly scalable example-based translation](https://link.springer.com/chapter/10.1007/978-3-540-30194-3_4) (Brown, AMTA 2004)

• A modified Burrows-Wheeler transform for highly scalable example-based translation (Brown, AMTA 2004)

ACL

• Ralf D. Brown. 2004. A modified Burrows-Wheeler transform for highly scalable example-based translation. In Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 27–36, Washington, USA. Springer.