V.: Syntax-driven Learning of Sub-sentential Translation Equivalents and Translation Rules from Parsed Parallel Corpora (2008)
| Venue: | In: Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation (SSST-2 |
| Citations: | 9 - 4 self |
BibTeX
@INPROCEEDINGS{Lavie08v.:syntax-driven,
author = {Alon Lavie and Alok Parlikar and Vamshi Ambati},
title = {V.: Syntax-driven Learning of Sub-sentential Translation Equivalents and Translation Rules from Parsed Parallel Corpora},
booktitle = {In: Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation (SSST-2},
year = {2008}
}
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Abstract
We describe a multi-step process for automatically learning reliable sub-sentential syntactic phrases that are translation equivalents of each other and syntactic translation rules between two languages. The input to the process is a corpus of parallel sentences, word-aligned and annotated with phrase-structure parse trees. We first apply a newly developed algorithm for aligning parse-tree nodes between the two parallel trees. Next, we extract all aligned sub-sentential syntactic constituents from the parallel sentences, and create a syntax-based phrase-table. Finally, we treat the node alignments as tree decomposition points and extract from the corpus all possible synchronous parallel tree fragments. These are then converted into synchronous context-free rules. We describe the approach and analyze its application to Chinese-English parallel data. 1
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