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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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Cites
BLEU: a method for automatic evaluation of machine translation
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SRILM - an extensible language modeling toolkit
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A Systematic Comparison of Various Statistical Alignment Models
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Bleu: a method for automatic evaluation of machine translation
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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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The Excitement Open Platform for Textual Inferences
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“Moses (Koehn et al., 2007) is used to build our phrase-based MT system.”
MethodText Reuse Detection Using a Composition of Text Similarity Measures
Bär, D., Zesch, T., & Gurevych, I. (2012) · TUbilio (Technical University of Darmstadt) · doi:10.17185/duepublico/72183 · Cited by 55
English–Mizo Machine Translation using neural and statistical approaches
Pathak, A., Pakray, P., & Bentham, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3601-3 · Cited by 55
“Though NMT systems are sophisticated, they need not necessarily overpower conventional Phrase-Based Statistical Machine Translation (PBSMT) systems [21], for all language pairs.”
BackgroundExploiting Monolingual Data at Scale for Neural Machine Translation
Wu, L., Wang, Y., Xia, Y., Qin, T., Lai, J., & Liu, T. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1430 · Cited by 51
“All datasets are tokenized with Moses (Koehn et al., 2007) toolkit 2 .”
MethodWang, Y., Arya, K., Kogias, M., Vanga, M., Bhandari, A., Yadwadkar, N. J., Sen, S., Elnikety, S., Kozyrakis, C., & Bianchini, R. (2021) · European Conference on Computer Systems · doi:10.1145/3447786.3456225 · Cited by 55
“moses [43] is amachine translation application written in C++.”
Metrics for MT evaluation: evaluating reordering
Birch, A., Osborne, M., & Blunsom, P. (2010) · Machine Translation · doi:10.1007/s10590-009-9066-5 · Cited by 55
Self-citationUnsupervised tokenization for machine translation
Chung, T., & Gildea, D. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699571.1699606 · Cited by 55
When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)
Huang, L., Zhao, K., & Ma, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1227 · Cited by 59
“With 2.56M training sentence pairs, Shen et al. (2016) reported 32.7 BLEU on the same dev set using Moses and 30.7 BLEU using the baseline RNNsearch (GroundHog) with beam size 10 (without BPE, without length normalization or length reward).”
MethodInfluentialAdaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation
Zaremoodi, P., Buntine, W., & Haffari, G. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2104 · Cited by 55
Statistical Post-Editing for a Statistical MT System
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Biological data sciences in genome researchPreprint
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Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence PredictionPreprint
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Investigations on Translation Model Adaptation Using Monolingual Data
Lambert, P., Schwenk, H., Servan, C., & Rauf, S. A. (2011) · HAL · Cited by 54
Using Moses to integrate multiple rule-based machine translation engines into a hybrid system
Eisele, A., Federmann, C., Saint-Amand, H., Jellinghaus, M., Herrmann, T., & Chen, Y. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626422 · Cited by 54
Data-Driven Machine Translation for Sign Languages
Morrissey, S. (2008) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 54
Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment
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Self-citationBiasing Attention-Based Recurrent Neural Networks Using External Alignment Information
Alkhouli, T., & Ney, H. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4711 · Cited by 55
Implicitation of Discourse Connectives in (Machine) Translation
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Iterative, MT-based sentence alignment of parallel texts
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Cohesive Phrase-Based Decoding for Statistical Machine Translation
Cherry, C. A. (2008) · Cited by 53
Large-Scale Syntactic Language Modeling with Treelets
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Inducing a Discriminative Parser to Optimize Machine Translation Reordering
Neubig, G., Watanabe, T., & Mori, S. (2012) · Cited by 53
Analysing the Effect of Out-of-Domain Data on SMT Systems
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Self-citationUsing SMT for OCR Error Correction of Historical Texts
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Neural Machine Translation With Sentence-Level Topic Context
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“The baselines systems include the PBSMT [26], vanilla attentional NMT (ANMT) [9], and Transformer-based NMT [12].”
MethodInfluentialPre-trained language model representations for language generation
Edunov, S., Baevski, A., & Auli, M. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1409 · Cited by 122
Turkish and its challenges for language processing
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Large Scale Decipherment for Out-of-Domain Machine Translation
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(2009) · Cited by 53
A Unified Framework for Phrase-Based, Hierarchical, and Syntax-Based Statistical Machine Translation
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Online learning for effort reduction in interactive neural machine translation
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Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation
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Document-level Consistency Verification in Machine Translation
Xiao, T., Zhu, J., Yao, S., & Zhang, H. (2011) · Cited by 51
Real-time Incremental Speech-to-Speech Translation of Dialogs
Bangalore, S., Sridhar, V. K. R., Kolan, P., Golipour, L., & Jimenez, A. (2012) · Cited by 51
A web-based interactive computer aided translation tool
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“For the FYelp and Amazon datasets, we normalize, lowercase and tokenize reviews using the moses (Koehn et al., 2007) tokenizer.”
MethodTravatar: A Forest-to-String Machine Translation Engine based on Tree Transducers
Neubig, G. (2013) · Cited by 50
Ramabhadran, B., Khudanpur, S. P., & Arısoy, E. (2012) · North American Chapter of the Association for Computational Linguistics · Cited by 50
Neural Cross-Lingual Event Detection with Minimal Parallel Resources
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“…(MT) systems trained on large numbers of parallel sentences or manually aligned documents to achieve a decent performance — the required parallel resources may only exist for a small fraction of language pairs (Koehn et al., 2007), which greatly limits the applicability of these methods.”
BackgroundSentence2SignGesture: a hybrid neural machine translation network for sign language video generation
Natarajan, B., Elakkiya, R., & Prasad, M. L. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03640-9 · Cited by 53
“The Statistical Machine translation models are introduced by the researchers (Koehn et al. 2005; Carpuat et al. 2007; Utiyama et al. 2007; Koehn et al. 2007; Bengio et al. 2003;Chiang et al. 2005), Phrase-Based Machine Translation (PBMT) (Wang et al. 2007; Pust et al. 2015), and Syntax-Based…”
MethodMachine translation and its evaluation: a study
Mondal, S. K., Zhang, H., Kabir, H. M. D., Ni, K., & Dai, H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10423-5 · Cited by 62
Design of the moses decoder for statistical machine translation
Hoang, H., & Koehn, P. (2008) · doi:10.3115/1622110.1622120 · Cited by 50
Self-citationFindings of the WMT 2016 Bilingual Document Alignment Shared Task
Buck, C., & Koehn, P. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2347 · Cited by 50
Self-citationRich source-side context for statistical machine translation
Gimpel, K., & Smith, N. A. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626396 · Cited by 50
Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation
Wang, R., Utiyama, M., Finch, A., Liu, L., Chen, K., & Sumita, E. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2837223 · Cited by 48
“In the IWSLT EN-FR task in Table III: 1) Directly using out-of-domain corpus, or adding all out- of-domain corpus to in-domain corpus, degraded both the performances of PBSMT and NMT. 2) Models trained by using additional pseudo in-domain data selected by δf , δe , and δfe gained modest improvements (0.8–3.2 BLEU poin…”
ResultInfluentialEnabling Code-Mixed Translation: Parallel Corpus Creation and MT Augmentation Approach
Dhar, M. K., Kumar, V., & Shrivastava, M. (2018) · Cited by 49
Tera-scale translation models via pattern matching
Lopez, A. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599145 · Cited by 49
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Felice, M., & Specia, L. (2012) · Cited by 49
Cho, E., Niehues, J., & Waibel, A. (2012) · KITopen · doi:10.5445/ir/1000145022 · Cited by 49
Kajiwara, T., & Komachi, M. (2016) · International Conference on Computational Linguistics · Cited by 49
Validation of an Automatic Metric for the Accuracy of Pronoun Translation (APT)
Werlen, L. M., & Popescu-Belis, A. (2017) · Workshop on Discourse in Machine Translation (DiscoMT) · doi:10.18653/v1/w17-4802 · Cited by 49
“The alignment is made both in direct (source-target) and reverse (target-source) directions, which are then merged using the grow-diag-final heuristic from Moses (Koehn et al., 2007).”
MethodCan Neural Machine Translation be Improved with User Feedback?
Kreutzer, J., Khadivi, S., Matusov, E., & Riezler, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers) · doi:10.18653/v1/n18-3012 · Cited by 49
“For significance tests we used approximate randomization (Clark et al., 2011), for BLEU score evaluation (lowercased) the multi-bleu script of the Moses decoder (Koehn et al., 2007), for TER computation the tercom tool (Snover et al., 2006).”
Li, T. J.-J., & Riva, O. (2018) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3210240.3210339 · Cited by 53
“3using the BLEU implementation in Moses [46] using n-grams up to N = 4, which is consistent with [63].”
MethodNaver Labs Europe’s Systems for the WMT19 Machine Translation Robustness Task
Berard, A., Calapodescu, I., & Roux, C. (2019) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w19-5361 · Cited by 49
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation
Le, H., Pino, J., Wang, C., Gu, J., Schwab, D., & Besacier, L. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.314 · Cited by 49
Language processing in brains and deep neural networks: computational convergence and its limitsPreprint
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On the mono- and cross-language detection of text reuse and plagiarism
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Effects of Empty Categories on Machine Translation
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The Best Lexical Metric for Phrase-Based Statistical MT System Optimization
Cer, D. M., Manning, C. D., & Jurafsky, D. S. (2010) · Cited by 48
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Hindi-to-Urdu Machine Translation through Transliteration
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Hybrid Parallel Sentence Mining from Comparable Corpora
Stefanescu, D. C., Ion, R., & Hunsicker, S. (2012) · Cited by 48
A Class-Based Agreement Model for Generating Accurately Inflected Translations
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Using syntax to improve word alignment precision for syntax-based machine translation
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System Combination for Grammatical Error Correction
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Empirical Comparison of Evaluation Methods for Unsupervised Learning of Morphology
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Artificial Error Generation with Machine Translation and Syntactic Patterns
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“Decoding is performed us-ing Moses (Koehn et al., 2007) and the language model used during decoding is built from the original erroneous sentences in the learner corpus.”
MethodKlubička, F., Toral, A., & Sánchez-Cartagena, V. M. (2018) · Machine Translation · doi:10.1007/s10590-018-9214-x · Cited by 48
Overview and Results of Morpho Challenge 2009
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Measuring machine translation quality as semantic equivalence: A metric based on entailment features
Padó, S., Cer, D., Galley, M., Jurafsky, D., & Manning, C. D. (2009) · Machine Translation · doi:10.1007/s10590-009-9060-y · Cited by 47
Connecting the Dots: Towards Human-Level Grammatical Error Correction
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“We use the popular phrase-based SMT toolkit Moses (Koehn et al., 2007), which employs a log-linear model for combination of features.”
MethodCache-based Online Adaptation for Machine Translation Enhanced Computer Assisted Translation
Bertoldi, N., Cettolo, M., & Federico, M. (2013) · Cited by 47
Self-citationEmpowering Qualitative Research Methods in Education with Artificial Intelligence
Longo, L. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-31787-4_1 · Cited by 59
SyMGiza++: Symmetrized Word Alignment Models for Statistical Machine Translation
Junczys-Dowmunt, M., & Szał, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-25261-7_30 · Cited by 47
Recent efforts in spoken language translation
Casacuberta, F., Federico, M., Ney, H., & Vidal, E. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.917989 · Cited by 47
Self-citationError Detection for Statistical Machine Translation Using Linguistic Features
Xiong, D., Zhang, M., & Li, H. (2010) · Cited by 47
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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