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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
Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385
A Systematic Comparison of Various Statistical Alignment Models
Och, F. J., & Ney, H. (2003) · Computational Linguistics · doi:10.1162/089120103321337421 · Cited by 3,855
Bleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,668
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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“May et al. (2014) used a phrase-based SMT system similar to Moses (Koehn et al., 2007), trained on the collection of Arabic-English parallel corpora The translation results between Arabic and English are up to 9.89 (BLEU score, after manual transliteration). van der Wees et al. (2016) built an SMT system.”
InfluentialA Survey of Domain Adaptation for Neural Machine TranslationPreprint
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“…et al., 2014; Bahdanau et al., 2015) allows for end-to-end training of a translation system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are characteristics of statistical machine translation (SMT) systems (Koehn et al., 2007).”
BackgroundCharacter-based Neural Machine TranslationPreprint
Ling, W., Trancoso, I. M., Dyer, C., & Black, A. W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04586 · Cited by 267
Self-citationA Survey of Automated Text Simplification
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MethodMultilingual sentiment analysis: from formal to informal and scarce resource languages
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“All the data were Unicode-normalized in a consistent way before being tokenized using Moses tokenizer (Koehn et al., 2007).”
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“We tokenized all data using the Moses toolkit (Koehn et al., 2007); for Uzbek-English experiments, we also truecased the data.”
MethodDomain adaptation for statistical machine translation with monolingual resources
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MethodIncorporating Structural Alignment Biases into an Attentional Neural Translation Model
Cohn, T., Hoang, C. D. V., Vylomova, E., Yao, K., Dyer, C., & Haffari, G. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1102 · Cited by 166
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Document Context Neural Machine Translation with Memory Networks
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A Continuously Growing Dataset of Sentential Paraphrases
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MethodShakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models
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MethodInfluentialSubjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs
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N-gram Counts and Language Models from the Common Crawl
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Generalizing Word Lattice Translation
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Self-citationActive Learning and Crowd-Sourcing for Machine Translation
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Discriminative reordering with Chinese grammatical relations features
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An Attentional Model for Speech Translation Without Transcription
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Self-citationTowards String-To-Tree Neural Machine Translation
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“We compute BLEU scores using the mteval-v13a.pl script from the Moses toolkit (Koehn et al., 2007 1, for the resource-rich setting, the single models (bpe2bpe, bpe2tree) perform similarly in terms of BLEU on new-stest2015.”
MethodCheap Translation for Cross-Lingual Named Entity Recognition
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The IIT Bombay English-Hindi Parallel Corpus
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Improved statistical machine translation using monolingually-derived paraphrases
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Self-citationWhat Level of Quality Can Neural Machine Translation Attain on Literary Text?
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Barrault, L., Bougares, F., Specia, L., Lala, C., Elliott, D., & Frank, S. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6402 · Cited by 142
“, 2018), and AFRL-OHIOSTATE 1 4COMBO U is a systems combination of the VMT system along with instantiations of Marian, OpenNMT, and Moses (Koehn et al., 2007).”
MethodInfluentialThe Prague Bulletin of Mathematical Linguistics
Schlesinger, P., Calzolari, N., von Hahn, W., Joshi, T. A., Peregrin, P. J., Rosen, P. A., & Sgall, P. P. (2009) · Cited by 142
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation DirectionsPreprint
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Self-citationDensely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
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“We tokenzie Czech and German using the Moses tokenizer 4 .”
MethodMachine translation from text to sign language: a systematic review
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“Mishra et al. translated English to ISL glosses using a word-based translation model, and the methodologies are implemented on MOSES [84].”
BackgroundMethodKasture, H., & Sánchez, D. (2014) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2541940.2541944 · Cited by 138
PET: a Tool for Post-editing and Assessing Machine Translation
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“We followed the standard WMT preprocessing recipes1, which are based on the Moses library (Koehn et al., 2007).”
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Learning Continuous Phrase Representations for Translation Modeling
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Data-Driven Sentence Simplification: Survey and Benchmark
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Self-citationUnderstanding and Improving Transformer From a Multi-Particle Dynamic System Point of ViewPreprint
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Sponge Examples: Energy-Latency Attacks on Neural Networks
Shumailov, I., Zhao, Y., Bates, D., Papernot, N., Mullins, R., & Anderson, R. (2021) · 2021 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp51992.2021.00024 · Cited by 125
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting
Hu, J. E., Khayrallah, H., Culkin, R., Xia, P., Chen, T., Post, M., & Durme, B. V. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1090 · Cited by 126
“For the Turkish text, we tokenize and truecase the data using scripts from Moses (Koehn et al., 2007).”
MethodModelling Pronominal Anaphora in Statistical Machine Translation
Hardmeier, C., & Federico, M. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 113
Self-citationImagination improves Multimodal TranslationPreprint
Elliott, D., & Kádár, Á. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.04350 · Cited by 126
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Nguyen, K., Daumé III, H., & Boyd-Graber, J. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1153 · Cited by 110
“For English and German, we tokenize and clean sentences using Moses (Koehn et al., 2007).”
MethodA Tree Sequence Alignment-based Tree-to-Tree Translation Model
Zhang, M., Jiang, H., Aw, A. T., Li, H., Tan, C. L., & Li, S. (2008) · Cited by 112
Curriculum Learning for Domain Adaptation in Neural Machine Translation
Zhang, X., Shapiro, P., Kumar, G., McNamee, P., Carpuat, M. J., & Duh, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1189 · Cited by 115
“Data Preprocessing All datasets are tokenized using the Moses (Koehn et al., 2007) tokenizer.”
MethodMultilingual Sentiment Analysis using Machine Translation
Balahur, A., & Turchi, M. (2012) · Cited by 111
On the use of comparable corpora to improve SMT performance
Abdul-Rauf, S., & Schwenk, H. (2009) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1609067.1609068 · Cited by 109
Context Gates for Neural Machine Translation
Tu, Z., Liu, Y., Lu, Z., Liu, X., & Li, H. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00048 · Cited by 126
A phrase-based alignment model for natural language inference
MacCartney, B., Galley, M., & Manning, C. D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613817 · Cited by 108
Ravi, S., & Knight, K. K. (2012) · Cited by 107
Interactive neural machine translation
Peris, Á., Domingo, M., & Casacuberta, F. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2016.12.003 · Cited by 108
“SMT based on stochastic finite state models also played a significant role in previous IMT works (Barrachina et al., 2009); but the prevailing SMT technology were PB systems (Koehn et al., 2007).”
Yang, Y., Huang, L., & Ma, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1342 · Cited by 121
“Unlike phrase-based MT systems which enjoy the benefits of very large beam sizes (in the order of 100–500) (Koehn et al., 2007) , most NMT systems choose tiny beam sizes up to 5; for example, Google’s GNMT (Wu et al.”
BackgroundUnsupervised Opinion Summarization as Copycat-Review Generation
Bražinskas, A., Lapata, M., & Titov, I. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.461 · Cited by 127
“We used the Moses’ (Koehn et al., 2007) reversible tokenizer and true-caser.”
MethodEfficient Word Alignment with Markov Chain Monte Carlo
Östling, R., & Tiedemann, J. (2016) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2016-0013 · Cited by 106
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
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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.
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