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Cited by 817

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

HMM-based word alignment in statistical translation

Open access
Vogel, Stephan, Ney, Hermann, Tillmann, Christoph·1996·Proceedings of the 16th conference on Computational linguistics -

Abstract

In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach uses a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment problem. The difference to the time alignment HMM is that there is no monotony constraint for the possible word orderings. We describe the details of the model and test the model on several bilingual corpora.

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Metadata

Authors
Vogel, S., Ney, H., & Tillmann, C.
Venue
Proceedings of the 16th conference on Computational linguistics -
Published
1996
Volume
2
Pages
836
ISSN
1525-2477 (ISSN-L)
OpenAlex
W2038698865
Cited by
817
References
4
FWCI
6.87
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: HMM-based word alignment in statistical translation. https://app.cochat.ai/papers/10.3115/993268.993313
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Vogel, S., Ney, H., & Tillmann, C. (1996). HMM-based word alignment in statistical translation. Proceedings of the 16th conference on Computational linguistics -. https://doi.org/10.3115/993268.993313

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.