The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Open accessAbstract
Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT).In particular, at the IWSLT 2015 evaluation campaign, NMT outperformed well established state-ofthe-art PBMT systems on English-German, a language pair known to be particularly hard because of morphology and syntactic differences.To understand in what respects NMT provides better translation quality than PBMT, we perform a detailed analysis of neural vs. phrase-based SMT outputs, leveraging high quality post-edits performed by professional translators on the IWSLT data.For the first time, our analysis provides useful insights on what linguistic phenomena are best modeled by neural models -such as the reordering of verbs -while pointing out other aspects that remain to be improved.
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
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
A Study of Translation Edit Rate with Targeted Human Annotation
Snover, M., Dorr, B. J., Schwartz, R. M., Micciulla, L., & Makhoul, J. I. (2006) · Conference of the Association for Machine Translation in the Americas · Cited by 2,372
A Simple, Fast, and Effective Reparameterization of IBM Model 2
Dyer, C., Chahuneau, V., & Smith, N. A. (2018) · doi:10.1184/r1/6472985 · Cited by 767
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
WIT3: Web Inventory of Transcribed and Translated Talks
Cettolo, M., Girardi, C., & Federico, M. (2012) · Cited by 543
Self-citationStanford neural machine translation systems for spoken language domains.
Luong, M.-T., & Manning, C. D. (2015) · Cited by 402
Findings of the 2015 Workshop on Statistical Machine Translation
Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., et al. (2015) · Proceedings of the Tenth Workshop on Statistical Machine Translation · doi:10.18653/v1/w15-3001 · Cited by 277
Error Analysis of Statistical Machine Translation Output
Vilar, D., Xu, J., D’Haro, L. F., & Ney, H. (2006) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/453fcbexxngk · Cited by 257
Accelerated DP based search for statistical translation
Tillmann, C., Vogel, S., Ney, H., Zubiaga, A., & Sawaf, H. (1997) · 5th European Conference on Speech Communication and Technology (Eurospeech 1997) · doi:10.21437/eurospeech.1997-673 · Cited by 232
An “AI readability ” formula for French as a foreign language
François, T., & Fairon, C. (2013) · Cited by 110
Cited by
What do Neural Machine Translation Models Learn about Morphology?
Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., & Glass, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1080 · Cited by 307
An Analysis of Encoder Representations in Transformer-Based Machine Translation
Raganato, A., & Tiedemann, J. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5431 · Cited by 277
Analyzing Uncertainty in Neural Machine Translation
Ott, M., Auli, M., Grangier, D., & Ranzato, M. (2018) · arXiv · doi:10.48550/arxiv.1803.00047 · Cited by 140
This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models
García-Ferrero, I., Altuna, B., Álvez, J., Gonzalez-Dios, I., & Rigau, G. (2023) · arXiv · doi:10.48550/arxiv.2310.15941 · Cited by 18
Unsupervised Quality Estimation for Neural Machine Translation
Fomicheva, M., Sun, S., Yankovskaya, L., Blain, F., Guzmán, F., Fishel, M., Aletras, N., Chaudhary, V., & Specia, L. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00330 · Cited by 10
Metadata
- Authors
- Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 257-267
- OpenAlex
- W2513073850
- Cited by
- 316
- References
- 24
- FWCI
- 24.14
- 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: Neural versus Phrase-Based Machine Translation Quality: a Case Study. https://app.cochat.ai/papers/10.18653/v1/d16-1025
- The paper
- Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus Phrase-Based Machine Translation Quality: a Case Study. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d16-1025
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.
