The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Open accessAbstract
We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
The mathematics of statistical machine translation: parameter estimation
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993) · Cited by 3,973
A Systematic Comparison of Various Statistical Alignment Models
Och, F. J., & Ney, H. (2003) · Computational Linguistics · doi:10.1162/089120103321337421 · Cited by 3,854
Minimum error rate training in statistical machine translation
Och, F. J. (2003) · Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03 · doi:10.3115/1075096.1075117 · Cited by 2,725
A statistical approach to machine translation
Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J., Mercer, R. L., & Roossin, P. S. (1990) · doi:10.5555/92858.92860 · Cited by 1,655
Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval
Robertson, S. E., & Walker, S. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_24 · Cited by 1,477
Statistical Significance Tests for Machine Translation Evaluation.
Koehn, P. (2004) · Cited by 1,449
Computational linguistics in 1990
Karlgren, H. (1990) · Proceedings of the 13th conference on Computational linguistics - · doi:10.3115/992507.992531 · Cited by 1,243
Generalized Iterative Scaling for Log-Linear Models
Darroch, J. N., & Ratcliff, D. (1972) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177692379 · Cited by 1,206
Discriminative training and maximum entropy models for statistical machine translation
Och, F. J., & Ney, H. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073133 · Cited by 1,034
Cited by
Domain Adaptation for Statistical Classifiers
Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816
Self-citationA Survey of Paraphrasing and Textual Entailment Methods
Androutsopoulos, I., & Malakasiotis, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2985 · Cited by 178
Metadata
- Authors
- Munteanu, D. S., & Marcu, D.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2005-12
- Volume
- 31
- Issue
- 4
- Pages
- 477-504
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2107695330
- Cited by
- 391
- References
- 24
- FWCI
- 7.90
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Improving Machine Translation Performance by Exploiting Non-Parallel Corpora. https://app.cochat.ai/papers/10.1162/089120105775299168
- The paper
- Munteanu, D. S., & Marcu, D. (2005). Improving Machine Translation Performance by Exploiting Non-Parallel Corpora. Computational Linguistics. https://doi.org/10.1162/089120105775299168
Data sources
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.
