The DOI matches its OpenAlex record; 2 of the 2 references it cites were found in a registry or an index.
Error Analysis of Statistical Machine Translation Output
Open accessAbstract
Evaluation of automatic translation output is a difficult task.Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system.However the interpretation of all of these measures is not at all clear, and the identification of the most prominent source of errors in a given system using these measures alone is not possible.Therefore some analysis of the generated translations is needed in order to identify the main problems and to focus the research efforts.This area is however mostly unexplored and few works have dealt with it until now.In this paper we will present a framework for classification of the errors of a machine translation system and we will carry out an error analysis of the system used by the RWTH in the first TC-STAR evaluation.
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
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
Doddington, G. (2002) · Proceedings of the second international conference on Human Language Technology Research - · doi:10.3115/1289189.1289273 · Cited by 1,584
Cited by
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1025 · Cited by 316
Guerreiro, N. M., Voita, E., & Martins, A. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.75 · Cited by 63
Metadata
- Authors
- Vilar, D., Xu, J., D’Haro, L. F., & Ney, H.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2006-05-01
- Pages
- 697-702
- OpenAlex
- W2463396630
- Cited by
- 257
- References
- 2
- FWCI
- 4.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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: Error Analysis of Statistical Machine Translation Output. https://app.cochat.ai/papers/10.63317/453fcbexxngk
- The paper
- Vilar, D., Xu, J., D’Haro, L. F., & Ney, H. (2006). Error Analysis of Statistical Machine Translation Output. Language Resources and Evaluation Conference (LREC). https://doi.org/10.63317/453fcbexxngk
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.