The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Alignment by agreement
Open accessAbstract
We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions.
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Cites
The complexity of computing the permanent
Valiant, L. G. (1979) · Theoretical Computer Science · doi:10.1016/0304-3975(79)90044-6 · Cited by 2,705
Cited by
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
“Nevertheless, as shown in Table 6, we were able to achieve AER scores comparable to the one-to-many alignments obtained by the Berkeley aligner (Liang et al., 2006).16 We also found that the alignments produced by local attention models achieve lower AERs than those of the global one.”
MethodTransfer Learning for Low-Resource Neural Machine Translation
Zoph, B., Yuret, D., May, J., & Knight, K. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1163 · Cited by 788
Leveraging Linguistic Structure For Open Domain Information Extraction
Angeli, G., Johnson Premkumar, M. J., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1034 · Cited by 725
Effective Approaches to Attention-based Neural Machine Translation
Luong, M.-T., Pham, H., & Manning, C. D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.04025 · Cited by 655
Findings of the 2016 Conference on Machine Translation
Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huck, M., Jimeno Yepes, A., Koehn, P., Logacheva, V., Monz, C., et al. (2016) · Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers · doi:10.18653/v1/w16-2301 · Cited by 539
Metadata
- Authors
- Liang, P., Taskar, B., & Klein, D.
- Venue
- Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -
- Published
- 2006
- Pages
- 104-111
- OpenAlex
- W2169724380
- Cited by
- 421
- References
- 1
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Liang, P., Taskar, B., & Klein, D. (2006). Alignment by agreement. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -. https://doi.org/10.3115/1220835.1220849
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
