The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Scalable inference and training of context-rich syntactic translation models
Open accessAbstract
Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syntactic approaches seek to remedy these problems. In this paper, we take the framework for acquiring multi-level syntactic translation rules of (Galley et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple interpretations of unaligned words. Second, we propose probability estimates and a training procedure for weighting these rules. We contrast different approaches on real examples, show that our estimates based on multiple derivations favor phrasal re-orderings that are linguistically better motivated, and establish that our larger rules provide a 3.63 BLEU point increase over minimal rules.
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
A hierarchical phrase-based model for statistical machine translation
Chiang, D. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219873 · Cited by 1,137
The Alignment Template Approach to Statistical Machine Translation
Och, F. J., & Ney, H. (2004) · Computational Linguistics · doi:10.1162/0891201042544884 · Cited by 918
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Wu, D. (1997) · doi:10.5555/972705.972707 · Cited by 827
A syntax-based statistical translation model
Yamada, K., & Knight, K. (2001) · Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01 · doi:10.3115/1073012.1073079 · Cited by 728
Self-citationGalley, M., Hopkins, M., Knight, K., & Marcu, D. (2004) · doi:10.21236/ada460212 · Cited by 460
Self-citationPhrasal cohesion and statistical machine translation
Fox, H. J. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118732 · Cited by 164
Cited by
Statistical machine translation
(2010) · Choice Reviews Online · doi:10.5860/choice.47-6293 · Cited by 1,309
Transfer 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
Self-citationRecurrent Neural Network Grammars
Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1024 · Cited by 517
Does String-Based Neural MT Learn Source Syntax?
Shi, X., Padhi, I., & Knight, K. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1159 · Cited by 300
Self-citationSennrich, R., & Haddow, B. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1248 · Cited by 8
Metadata
- Authors
- Galley, M., Graehl, J., Knight, K., Marcu, D., DeNeefe, S., Wang, W., & Thayer, I.
- Venue
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
- Published
- 2006
- Pages
- 961-968
- OpenAlex
- W2165666205
- Cited by
- 414
- References
- 6
- FWCI
- 30.01
- Type
- conference paper
- Language
- en
- Open access
- gold
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: Scalable inference and training of context-rich syntactic translation models. https://app.cochat.ai/papers/10.3115/1220175.1220296
- The paper
- Galley, M., Graehl, J., Knight, K., Marcu, D., DeNeefe, S., Wang, W., & Thayer, I. (2006). Scalable inference and training of context-rich syntactic translation models. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06. https://doi.org/10.3115/1220175.1220296
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.
