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Moses: open source toolkit for statistical machine translation
Open accessAbstract
We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.
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“The RNN model gave higher BLEU4 scores over other compared baselines, phrase-based machine translation [34] and template-based method [37].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [34]
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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
SRILM - an extensible language modeling toolkit
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A Systematic Comparison of Various Statistical Alignment Models
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Statistical phrase-based translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073462 · Cited by 3,265
Self-citationMinimum error rate training in statistical machine translation
Och, F. J. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075117 · Cited by 2,728
Statistical Phrase-Based Translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 970
Self-citationPharaoh: A Beam Search Decoder for Phrase-Based Statistical Machine Translation Models
Koehn, P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30194-3_13 · Cited by 633
Self-citationKoehn, P., & Hoang, H. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 509
Self-citationLattice-based minimum error rate training for statistical machine translation
Macherey, W., Och, F. J., Thayer, I., & Uszkoreit, J. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613807 · Cited by 347
Research works printed but not linked: 2
- Axelrod, Amittai. "Factored Language Model for Sta- tistical Machine Translation." MRes Thesis. Edinburgh University, 2006.
- Koehn, Philipp, Marcello Federico, Wade Shen, Nicola Bertoldi, Ondrej Bojar, Chris Callison-Burch, Brooke Cowan, Chris Dyer, Hieu Hoang, Richard Zens, Alexandra Constantin, Christine Corbett Moran, and Evan Herbst. "Open Source Toolkit for Statistical Machine Transla- tion". Report of the 2006 Summer Workshop at Johns Hopkins University, 2006.
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“We use the Moses (Koehn et al., 2007) toolkit for tokenization and cleaning the English side of the data.”
MethodSpelling Correction of User Search Queries through Statistical Machine Translation
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Hybrid machine translation architectures within and beyond the EuroMatrix project
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HUME: Human UCCA-Based Evaluation of Machine Translation
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Adaptive Scheduling for Multi-Task LearningPreprint
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“perl from Moses [11].”
Controlling the Output Length of Neural Machine TranslationPreprint
Lakew, S. M., Gangi, M. D., & Federico, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10408 · Cited by 37
Self-citationEnriching Parallel Corpora for Statistical Machine Translation with Semantic Negation Rephrasing
Wetzel, D., & Bond, F. (2012) · Cited by 31
Disambiguating temporal-contrastive connectives for machine translation
Meyer, T. (2011) · Cited by 31
Learning to Interpret and Describe Abstract Scenes
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Sensespotting: Never let your parallel data tie you to an old domain
Carpuat, M. J., Daumé, H., Henry, K. E., Irvine, A., Jagarlamudi, J., & Rudinger, R. (2013) · Cited by 31
Forest-based tree sequence to string translation model
Zhang, H., Zhang, M., Li, H., Aw, A. T., & Tan, C. L. (2009) · doi:10.3115/1687878.1687904 · Cited by 31
Towards Effective Use of Training Data in Statistical Machine Translation
Koehn, P., & Haddow, B. (2012) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 31
Self-citationIncremental Segmentation and Decoding Strategies for Simultaneous Translation
Yarmohammadi, M., Sridhar, V. K. R., Bangalore, S., & Sankaran, B. (2013) · International Joint Conference on Natural Language Processing · Cited by 31
Hierarchical phrase-based translation with weighted finite state transducers
Iglesias, G., de Gispert, A., Banga, E. R., & Byrne, W. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620754.1620817 · Cited by 31
Learning Tractable Word Alignment Models with Complex Constraints
Graça, J., Ganchev, K., & Taskar, B. (2010) · Computational Linguistics · doi:10.1162/coli_a_00007 · Cited by 31
Additive Neural Networks for Statistical Machine Translation
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Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation
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Orthographic Syllable as basic unit for SMT between Related Languages
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Auto-Encoding Variational Neural Machine Translation
Eikema, B., & Aziz, W. F. (2019) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w19-4315 · Cited by 34
“Pre-processing We tokenized and truecased all data using standard scripts from the Moses toolkit (Koehn et al., 2007), and removed sentences longer than 50 tokens.”
MethodA Multilingual View of Unsupervised Machine Translation
Garcia, X., Foret, P., Sellam, T., & Parikh, A. P. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.283 · Cited by 38
Improving neural machine translation with sentence alignment learning
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Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
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A Semantic Feature for Statistical Machine Translation
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Nikoulina, V., Kovachev, B., Lagos, N., & Monz, C. (2012) · UvA-DARE (University of Amsterdam) · Cited by 31
Transliteration for Resource-Scarce Languages
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Al-Haj, H., & Lavie, A. (2011) · Machine Translation · doi:10.1007/s10590-011-9101-1 · Cited by 31
Exploiting query logs for cross-lingual query suggestions
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Self-citationIntegrating Translation Memory into Phrase-Based Machine Translation during Decoding
Wang, K., Zong, C., & Su, K. (2013) · Cited by 31
Word-Order Issues in English-to-Urdu Statistical Machine Translation
Jawaid, B., & Zeman, D. (2011) · The Prague Bulletin of Mathematical Linguistics · doi:10.2478/v10108-011-0007-0 · Cited by 31
Traditional and Emerging Use-Cases for Machine Translation
Way, A. (2013) · Cited by 31
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“First, a hierarchical phrase-based (HPB) translation system s built using Moses (Koehn et al., 2007) with standard features.”
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“The phrases and lexical reordering are extracted using the default values of Moses.”
InfluentialIncremental Coreference Resolution for German
Tuggener, D. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-124915 · Cited by 31
Incremental Adaptation Strategies for Neural Network Language Models
Ter-Sarkisov, A., Schwenk, H., Bougares, F., & Barrault, L. (2015) · doi:10.18653/v1/w15-4006 · Cited by 30
Experiments on domain adaptation for patent machine translation in the PLuTO project
Ceauşu, A., Tinsley, J. F., Zhang, J., & Way, A. (2011) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 30
Pragmatic Neural Language Modelling in Machine Translation
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Twitter hashtags: Joint translation and clustering
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The Structure of the EU Mediasphere
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Coping with the Subjectivity of Human Judgements in MT Quality Estimation
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Self-citationAUTOMATED TRANSLATION OF A LITERARY WORK: A PILOT STUDY
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Sentence Simplification as Tree Transduction
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Post-ordering by Parsing for Japanese-English Statistical Machine Translation
Goto, I., Utiyama, M., & Sumita, E. (2012) · Cited by 30
Normalization of Dutch User-Generated Content
Clercq, O. D., Schulz, S., Desmet, B., Lefever, E., & Hoste, V. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 30
An Algerian dialect: Study and Resources
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Scherrer, Y., & Ljubešić, N. (2016) · Archive ouverte UNIGE (University of Geneva) · Cited by 30
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
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Evaluating MT for massive open online courses
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“The PBSMT system used is Moses (Koehn et al. 2007), with MGIZA (Gao and Vogel 2008) used to train word alignments, and KenLM (Heafield 2011) for training and scoring the language models.”
MethodMachine Learning Based Optimized Pruning Approach for Decoding in Statistical Machine Translation
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Nguyen, Q.-P., Vo, A.-D., Shin, J.-C., Tran, P., & Ock, C.-Y. (2019) · IEEE Access · doi:10.1109/access.2019.2902270 · Cited by 31
Joanis, E., Knowles, R., Kühn, R., Larkin, S., Littell, P., Lo, C.-K., Stewart, D. A., & Micher, J. C. (2020) · Language Resources and Evaluation · Cited by 30
A Survey of Orthographic Information in Machine Translation
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“Statistical Machine Translation (SMT) (Brown et al., 1993; Koehn, 2005; Koehn et al., 2007; Koehn, 2010; Waite and Byrne, 2015) is one of the CBMT based systems.”
BackgroundMethodInfluentialDynamic Curriculum Learning for Low-Resource Neural Machine Translation
Xu, C., Hu, B., Jiang, Y., Feng, K., Wang, Z., Huang, S., Ju, Q., Xiao, T., & Zhu, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.352 · Cited by 31
Improving Transformer-Based Neural Machine Translation with Prior Alignments
Nguyen, T., Nguyen, L., Tran, P., & Nguyen, H. (2021) · Complex · doi:10.1155/2021/5515407 · Cited by 25
“Preparing token alignments is a crucial part of the traditional SMTmodels. most popular token alignment tool is Giza++ [11], which is used by default in the famous SMT system Moses [1].”
MethodInfluentialTraining Continuous Space Language Models: Some Practical Issues
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Statistical Machine Translation into a Morphologically Complex Language
Oflazer, K. (2008) · Springer eBooks · doi:10.1007/978-3-540-78135-6_32 · Cited by 30
A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation
Wang, L., Wong, D. F., Chao, L. S., Lu, Y., & Xing, J. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/745485 · Cited by 30
English to Bangla Phrase-Based Machine Translation
Islam, Z., Tiedemann, J., & Eisele, A. (2010) · Cited by 30
Learning performance of a machine translation system
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Facilitating Translation Using Source Language Paraphrase Lattices
Du, J., Jiang, J., & Way, A. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 30
Schwenk, H., & Sénellart, J. (2009) · Cited by 30
Proceedings of the Workshop on Language Analysis in Social Media
Danescu-Niculescu-Mizil, C., Farzindar, A., Gamon, M., Inkpen, D. Z., & Nagarajan, M. (2013) · Cited by 31
Manual labour: tackling machine translation for sign languages
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Using supertags as source language context in SMT
Haque, R., Naskar, S. K., Ma, Y., & Way, A. (2009) · Arrow@dit (Dublin Institute of Technology) · Cited by 30
Neural Headline Generation with Sentence-wise OptimizationPreprint
Ayana, A., Shen, S., Zhao, Y., Liu, Z., & Sun, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01904 · Cited by 30
Multi-Word Expression-Sensitive Word Alignment
Okita, T., Maldonado, A., Graham, Y., & Way, A. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 29
A comparison of merging strategies for translation of German compounds
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An Experimental Management System
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Self-citationExtracting Paraphrases from Definition Sentences on the Web
Hashimoto, C., Torisawa, K., Saeger, S. D., Kazama, J., & Kurohashi, S. (2011) · Cited by 29
Domain Adaptation in Statistical Machine Translation using Factored Translation Models
Niehues, J., & Waibel, A. (2010) · doi:10.5445/ir/1000030254 · Cited by 29
Building a Web-Based Parallel Corpus and Filtering Out Machine-Translated Text
Antonova, A., & Misyurev, A. (2011) · Cited by 29
Shallow Semantic Trees for SMT
Aziz, W. F., Rios, M., & Specia, L. (2011) · Cited by 29
The FBK Participation in the WMT15 Automatic Post-editing Shared Task
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Machine Translation Quality and Post-Editor Productivity
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Self-citationDynamic Data Selection for Neural Machine Translation
van der Wees, M., Bisazza, A., & Monz, C. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1147 · Cited by 54
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models
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“3The data comes from the GALE project; the largest bitexts are LDC2007E103 and LDC2006G05 Moses (Koehn et al., 2007), directly on the same data we used to train Joint-Seq2Seq, i.e. pairs of Chinese sentences and English linearized PredPatt.”
MethodThe University of Edinburgh’s Submissions to the WMT19 News Translation Task
Bawden, R., Bogoychev, N., Germann, U., Grundkiewicz, R., Kirefu, F., Barone, A. V. M., & Birch, A. (2019) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w19-5304 · Cited by 29
“We pre-processed all data using standard scripts from the Moses toolkit (Koehn et al., 2007): normalisation, tokenisation, cleaning (of training data only, with a maximum sentence length of 80 to-kens) and true-casing for English data, using a model trained on all available news data.”
MethodSelf-citationImproving Neural Machine Translation with Soft Template Prediction
Yang, J., Ma, S., Zhang, D.-D., Li, Z.-J., & Zhou, M. (2020) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.531 · Cited by 44
“We use Moses (Koehn et al., 2007) to tokenize English sentences and our in-house tool to tokenize Chinese sentences.”
Method
Metadata
- Authors
- Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., & Moran, C.
- Affiliations
- University of Edinburgh (GB)
- RWTH Aachen University (DE)
- University of Maryland, College Park (US)
- Charles University (CZ)
- Williams College (US)
- Cornell University (US)
- Venue
- Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07
- Published
- 2007
- Pages
- 177
- OpenAlex
- W2124807415
- Cited by
- 4,735
- References
- 16
- Citations by year
- 2026: 18 · 2025: 35 · 2024: 44 · 2023: 73 · 2022: 89 · 2021: 198 · 2020: 268 · 2019: 299 · 2018: 335 · 2017: 291 · 2016: 396 · 2015: 387 · 2014: 440 · 2013: 389 · 2012: 418
- FWCI
- 154.31
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- statistical machine translation, open-source software toolkit, translation models, language models, data formats
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 ›
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