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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
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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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Predicting success in machine translation
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Coarse-to-Fine Natural Language Processing
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“We tokenize all text with the Moses tokenizer (Koehn et al., 2007), which treats punc-tuation marks as separate tokens but otherwise does not break words into smaller units, and we then analyze n -grams formed from these to-kens.”
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“We tokenize all text with the Moses tokenizer (Koehn et al., 2007), which treats punc-tuation marks as separate tokens but otherwise does not break words into smaller units, and we then analyze n -grams formed from these to-kens.”
Neural machine translation: Challenges, progress and future
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Multilingual Neural Machine Translation with Task-Specific AttentionPreprint
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“The source and target sides of the parallel training data are processed with the standard tokenizer included in the Moses SMT Toolkit (Koehn et al., 2007 training multilingual systems, we merge the parallel data for all available directions.”
MethodToward Statistical Machine Translation without Parallel Corpora
Klementiev, A., Irvine, A., Callison-Burch, C., & Yarowsky, D. (2012) · Cited by 68
Self-citationSubmodularity for Data Selection in Machine Translation
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A Context-Aware Recurrent Encoder for Neural Machine Translation
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“We compared our proposed model against the following two widely-used systems: 1) Moses [20]: an open source state-of-the-art phrase-based SMT system.”
BackgroundMethodNeural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation
Bulté, B., & Tezcan, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1175 · Cited by 69
“As SMT baseline we train a Moses engine (Koehn et al., 2007) with the sentence pairs inM, using standard settings5.”
MethodUnsupervised cleansing of noisy text
Contractor, D., Faruquie, T. A., & Subramaniam, L. V. (2010) · International Conference on Computational Linguistics · Cited by 68
Multimodal Interactive Pattern Recognition and Applications
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Chinese Song Iambics Generation with Neural Attention-based ModelPreprint
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Paraphrase Generation: A Survey of the State of the Art
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Democratizing neural machine translation with OPUS-MT
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Improved Minimum Error Rate Training in Moses
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Self-citationA Character-Level Machine Translation Approach for Normalization of SMS Abbreviations
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Recurrent Neural Networks for Word Alignment Model
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Constrained Grammatical Error Correction using Statistical Machine Translation
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Arabic Dialect Handling in Hybrid Machine Translation
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Wider context by using bilingual language models in machine translation
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Improving Pronoun Translation for Statistical Machine Translation
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Generating English from Abstract Meaning Representations
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Overview of the 7th Workshop on Asian Translation
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Self-citationInvestigations on Large-Scale Lightly-Supervised Training for Statistical Machine Translation
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Can Text Simplification Help Machine Translation
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Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT
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“• cSMTiser 4 (Ljubeši´c et al., 2016; Scherrer and Ljubeši´c, 2016), which implements a normalization pipeline using character-based statistical machine translation (CSMT) using the Moses toolkit (Koehn et al., 2007).”
MethodEspresso: A Fast End-to-End Neural Speech Recognition Toolkit
Wang, Y., Chen, T., Xu, H., Ding, S., Lv, H., Shao, Y., Peng, N., Xie, L., Watanabe, S., & Khudanpur, S. P. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003968 · Cited by 65
“Moses [6], Joshua [7]) to end-to-end neural machine translation (NMT) systems (e.”
Background"Poetic" Statistical Machine Translation: Rhyme and Meter
Genzel, D., Uszkoreit, J., & Och, F. J. (2010) · Cited by 65
MT-based sentence alignment for OCR-generated parallel texts
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Segmentation Strategies for Streaming Speech Translation
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Interactive Attention for Neural Machine TranslationPreprint
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A Neural Approach to Source Dependence Based Context Model for Statistical Machine Translation
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“We use the standard natural language translation model from M ���� [27] to estimate these probabilities from a large, matched corpus of clear and mini � ed names, as we describe in more detail below in Section 3.”
MethodInfluentialFindings of the WMT 2019 Shared Task on Automatic Post-Editing
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Improving Translation Model by Monolingual Data
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Self-citationCross-Lingual Abstract Meaning Representation Parsing
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“3 As Google Translate has access to a much larger training corpus, we also trained baseline MT models using Moses (Koehn et al., 2007) and Nematus (Sennrich et al., 2017), with the same training data we use for the projection method and default hyper-parameters.”
MethodCombining Bilingual and Comparable Corpora for Low Resource Machine Translation
Irvine, A., & Callison-Burch, C. (2013) · Cited by 63
Self-citationWhat do professional translators think about post-editing?
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Applying dynamic Bayesian networks in transliteration detection and generation
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Self-citationIncorporating Source Syntax into Transformer-Based Neural Machine Translation
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Towards Cross-Lingual Textual Entailment
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“Here we instead created a small dictionary by us-ing all available parallel data from OPUS (Ubuntu, KDE and Gnome, a total of 350K text snip-pets), and training a statistical machine translation model using Moses (Koehn et al., 2007).”
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Using Sense-labeled Discourse Connectives for Statistical Machine Translation
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Statistical Machine Translation: A Guide for Linguists and Translators
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Instance Selection for Machine Translation using Feature Decay Algorithms
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Correcting Semantic Collocation Errors with L1-induced Paraphrases
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Context Adaptation in Statistical Machine Translation Using Models with Exponentially Decaying Cache
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Synthetic Treebanking for Cross-Lingual Dependency Parsing
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Self-citationMotivating Personality-aware Machine Translation
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Machine Translation of Labeled Discourse Connectives
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Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?
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Self-citationAn overview of the European Union’s highly multilingual parallel corpora
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A linguistically motivated taxonomy for Machine Translation error analysis
Costa, Â., Wang, L., Luís, T., Correia, R., & Coheur, L. (2015) · Machine Translation · doi:10.1007/s10590-015-9169-0 · Cited by 58
Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings
Chaudhary, V., Tang, Y., Guzmán, F., Schwenk, H., & Koehn, P. (2019) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w19-5435 · Cited by 58
“The quality of the resulting subsets is determined by the quality of a statistical machine translation (Moses, phrase-based (Koehn et al., 2007)) and the neural machine translation system fairseq (Ott et al., 2019) trained on this data.”
MethodSelf-citationJointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation
Guo, J., Xu, L.-L., & Chen, E.-H. (2020) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.36 · Cited by 84
“For each dataset, we tokenize the sentences by Moses (Koehn et al., 2007) and segment each word into subwords using Byte-Pair Encoding (BPE) (Sennrich et al., 2015), resulting in a 32 k vocabulary shared by source and target languages.”
MethodWeighting Finite-State Transductions With Neural Context
Rastogi, P., Cotterell, R., & Eisner, J. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1076 · Cited by 63
Human versus automatic quality evaluation of NMT and PBSMT
Shterionov, D., Superbo, R., Nagle, P., Casanellas, L., O’Dowd, T., & Way, A. (2018) · Machine Translation · doi:10.1007/s10590-018-9220-z · Cited by 57
Adaptive Multi-pass Decoder for Neural Machine Translation
Geng, X., Feng, X., Qin, B., & Liu, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1048 · Cited by 54
“Note that we used all data to train MOSES (Koehn et al., 2007).”
MethodPlan, Write, and Revise: an Interactive System for Open-Domain Story Generation
Goldfarb-Tarrant, S., Feng, H., & Peng, N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-4016 · Cited by 63
“Model output is naively detokenized via Moses (Koehn et al., 2007) based on feedback from users that this was more natural.”
BackgroundGenerating question titles for Stack Overflow from mined code snippets
Gao, Z., Xia, X., Grundy, J., Lo, D., & Li, Y.-F. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 57
Accurate Non-Hierarchical Phrase-Based Translation
Galley, M., & Manning, C. D. (2010) · Cited by 57
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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