An algorithm for word-level alignment of parallel dependency trees

Yuan Ding, Daniel Gildea, Martha Palmer


Abstract
Structural divergence presents a challenge to the use of syntax in statistical machine translation. We address this problem with a new algorithm for alignment of loosely matched non-isomorphic dependency trees. The algorithm selectively relaxes the constraints of the two tree structures while keeping computational complexity polynomial in the length of the sentences. Experimentation with a large Chinese-English corpus shows an improvement in alignment results over the unstructured models of (Brown et al., 1993).
Anthology ID:
2003.mtsummit-papers.13
Volume:
Proceedings of Machine Translation Summit IX: Papers
Month:
September 23-27
Year:
2003
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New Orleans, USA
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MTSummit
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URL:
https://aclanthology.org/2003.mtsummit-papers.13
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Cite (ACL):
Yuan Ding, Daniel Gildea, and Martha Palmer. 2003. An algorithm for word-level alignment of parallel dependency trees. In Proceedings of Machine Translation Summit IX: Papers, New Orleans, USA.
Cite (Informal):
An algorithm for word-level alignment of parallel dependency trees (Ding et al., MTSummit 2003)
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https://aclanthology.org/2003.mtsummit-papers.13.pdf