HAL: Challenging Three Key Aspects of IBM-style Statistical Machine Translation

Christer Samuelsson


Abstract
The IBM schemes use weighted cooccurrence counts to iteratively improve translation and alignment probability estimates. We argue that: 1) these cooccurrence counts should be combined differently to capture word correlation; 2) alignment probabilities adopt predictable distributions; and 3) consequently, no iteration is needed. This applies equally well to word-based and phrase-based approaches. The resulting scheme, dubbed HAL, outperforms the IBM scheme in experiments.
Anthology ID:
2012.amta-papers.15
Volume:
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 28-November 1
Year:
2012
Address:
San Diego, California, USA
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AMTA
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Publisher:
Association for Machine Translation in the Americas
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URL:
https://aclanthology.org/2012.amta-papers.15
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Cite (ACL):
Christer Samuelsson. 2012. HAL: Challenging Three Key Aspects of IBM-style Statistical Machine Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
Cite (Informal):
HAL: Challenging Three Key Aspects of IBM-style Statistical Machine Translation (Samuelsson, AMTA 2012)
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PDF:
https://aclanthology.org/2012.amta-papers.15.pdf