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 of [36] achieved higher BLEU4 scores than phrase-based machine translation [34] and a template-based method [37].
“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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“Each is tokenized using the Moses Toolkit [Koehn et al., 2007].”
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Scaling Neural Machine Translation
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“Moses statistical MT [113] is adopted which allows customizations of translation results.”
MethodNeural Machine Translation of Rare Words with Subword Units
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“For most languages, we use the moses tokenizer (Koehn et al., 2007).6 For Chinese we use the SacreBLEU tokenizer (tok zh) and convert all traditional characters generated by the model to simplified characters using HanziConv7 (Post, 2018),8 for Indian languages 2.…”
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“We tokenize all corpora using a script from Moses (Koehn et al., 2007) and segment each word into subword units using Byte Pair Encoding (BPE, Sennrich et al.”
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“• Moses [38] implements a statistical machine translation (SMT) system, a vital component of online translation systems and intelligent personal assistants (IPA), and is configured as outlined in Tailbench [37].”
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Progress in Machine Translation
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Specia, L., Frank, S., Sima’an, K., & Elliott, D. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2346 · Cited by 228 · see citing works
SRILM at Sixteen: Update and Outlook
Stolcke, A., Zheng, J., Wen, W., & Abrash, V. (2011) · Cited by 225 · see citing works
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
Chu, C., Dabre, R., & Kurohashi, S. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2061 · Cited by 215 · see citing works
Dyer, C., Lopez, A., Ganitkevitch, J., Weese, J., Setiawan, H., Türe, F., Eidelman, V., Blunsom, P., & Resnik, P. (2010) · Cited by 224 · see citing works
Self-citationNeural Machine Translation with Extended Context
Tiedemann, J., & Scherrer, Y. (2017) · Workshop on Discourse in Machine Translation (DiscoMT) · doi:10.18653/v1/w17-4811 · Cited by 219 · see citing works
Balahur, A., & Turchi, M. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.03.004 · Cited by 216 · see citing works
MultiUN: A Multilingual Corpus from United Nation Documents
Eisele, A., & Chen, Y. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5co5op3ipz42 · Cited by 211 · see citing works
Chinese Syntactic Reordering for Statistical Machine Translation
Wang, C., Collins, M., & Koehn, P. (2007) · Cited by 211 · see citing works
Self-citationVariational Neural Machine Translation
Zhang, B., Xiong, D., Su, J., Duan, H., & Zhang, M. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1050 · Cited by 211 · see citing works
Coherent Multi-sentence Video Description with Variable Level of Detail
Rohrbach, A., Rohrbach, M., Qiu, W., Friedrich, A., Pinkal, M., & Schiele, B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_15 · Cited by 209 · see citing works
Evaluating Discourse Phenomena in Neural Machine Translation
Bawden, R., Sennrich, R., Birch, A., & Haddow, B. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1118 · Cited by 210 · see citing works
Self-citationCharacter-based neural machine translation
Costa‐jussà, M. R., & Fonollosa, J. A. R. (2016) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 210 · see citing works
Paraphrasing Revisited with Neural Machine Translation
Mallinson, J., Sennrich, R., & Lapata, M. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1083 · Cited by 209 · see citing works
“The data was pre-processed using standard pre-processing scripts found in MOSES (Koehn et al., 2007).”
MethodIs Neural Machine Translation the New State of the Art?
Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J. F., & Way, A. (2017) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2017-0013 · Cited by 208 · see citing works
“Three different systems were compared in this experiment (1) a PBSMT baseline model built with the Moses SMT Toolkit (Koehn et al., 2007), (2) a text-only NMT model ( NMT t ), and (3) a multi-modal NMT model ( NMT m ), described in more detail in Calixto et al. (2017b), which expands upon the…”
MethodGuzmán, F., Chen, P., Ott, M., Pino, J., Lample, G., Koehn, P., Chaudhary, V., & Ranzato, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1632 · Cited by 231 · see citing works
“The PBSMT systems use Moses (Koehn et al., 2007), with state-of-the-art settings (5-gram language model, hierarchical lexicalized reordering model, operation sequence model) but no additional monolingual data to train the language model.”
MethodSelf-citationIncorporating Discrete Translation Lexicons into Neural Machine Translation
Arthur, P., Neubig, G., & Nakamura, S. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1162 · Cited by 205 · see citing works
A survey of research on text simplification
Siddharthan, A. (2014) · ITL Review of Applied Linguistics · doi:10.1075/itl.165.2.06sid · Cited by 202 · see citing works
An Information Retrieval Approach to Short Text ConversationPreprint
Ji, Z., Lu, Z., & Li, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6988 · Cited by 202 · see citing works
- 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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