@inproceedings{venugopal-etal-2008-wider,
title = "Wider Pipelines: N-Best Alignments and Parses in {MT} Training",
author = "Venugopal, Ashish and
Zollmann, Andreas and
Smith, Noah A. and
Vogel, Stephan",
booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 21-25",
year = "2008",
address = "Waikiki, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2008.amta-papers.18",
pages = "192--201",
abstract = "State-of-the-art statistical machine translation systems use hypotheses from several maximum a posteriori inference steps, including word alignments and parse trees, to identify translational structure and estimate the parameters of translation models. While this approach leads to a modular pipeline of independently developed components, errors made in these {``}single-best{''} hypotheses can propagate to downstream estimation steps that treat these inputs as clean, trustworthy training data. In this work we integrate N-best alignments and parses by using a probability distribution over these alternatives to generate posterior fractional counts for use in downstream estimation. Using these fractional counts in a DOP-inspired syntax-based translation system, we show significant improvements in translation quality over a single-best trained baseline.",
}
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<abstract>State-of-the-art statistical machine translation systems use hypotheses from several maximum a posteriori inference steps, including word alignments and parse trees, to identify translational structure and estimate the parameters of translation models. While this approach leads to a modular pipeline of independently developed components, errors made in these “single-best” hypotheses can propagate to downstream estimation steps that treat these inputs as clean, trustworthy training data. In this work we integrate N-best alignments and parses by using a probability distribution over these alternatives to generate posterior fractional counts for use in downstream estimation. Using these fractional counts in a DOP-inspired syntax-based translation system, we show significant improvements in translation quality over a single-best trained baseline.</abstract>
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%0 Conference Proceedings
%T Wider Pipelines: N-Best Alignments and Parses in MT Training
%A Venugopal, Ashish
%A Zollmann, Andreas
%A Smith, Noah A.
%A Vogel, Stephan
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F venugopal-etal-2008-wider
%X State-of-the-art statistical machine translation systems use hypotheses from several maximum a posteriori inference steps, including word alignments and parse trees, to identify translational structure and estimate the parameters of translation models. While this approach leads to a modular pipeline of independently developed components, errors made in these “single-best” hypotheses can propagate to downstream estimation steps that treat these inputs as clean, trustworthy training data. In this work we integrate N-best alignments and parses by using a probability distribution over these alternatives to generate posterior fractional counts for use in downstream estimation. Using these fractional counts in a DOP-inspired syntax-based translation system, we show significant improvements in translation quality over a single-best trained baseline.
%U https://aclanthology.org/2008.amta-papers.18
%P 192-201
Markdown (Informal)
[Wider Pipelines: N-Best Alignments and Parses in MT Training](https://aclanthology.org/2008.amta-papers.18) (Venugopal et al., AMTA 2008)
ACL
- Ashish Venugopal, Andreas Zollmann, Noah A. Smith, and Stephan Vogel. 2008. Wider Pipelines: N-Best Alignments and Parses in MT Training. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, pages 192–201, Waikiki, USA. Association for Machine Translation in the Americas.