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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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Formality Style Transfer with Shared Latent Space
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“We use Moses (Koehn et al., 2007) for our experiments.”
FBK-UEdin Participation to the WMT13 Quality Estimation Shared Task
de Souza, J. G. C., Buck, C., Turchi, M., & Negri, M. (2013) · Cited by 29
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Optimizing segmentation granularity for neural machine translation
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“For Czech, German and Turkish, we tokenize with the Moses tokenizer (Koehn et al. 2007).”
MethodMultimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
Chowdhury, K. D., Hasanuzzaman, M., & Liu, Q. (2018) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/w18-3405 · Cited by 29
“For training the Hi c (cid:0) En c corpus, we use the Moses SMT system (Koehn et al., 2007) .”
MethodRule-Based Augmentation of Training Data in Breton-French Statistical Machine Translation
Tyers, F. M. (2009) · Cited by 29
Statistical Machine Translation with Readability Constraints
Stymne, S., Tiedemann, J., Hardmeier, C., & Nivre, J. (2013) · DSpace repository (University of Tartu) · Cited by 29
Machine Transliteration of Proper Names
Matthews, D. (2008) · Cited by 29
Machine translation by pattern matching
Resnik, P., & Lopez, A. (2008) · University Libraries (University of Maryland) · Cited by 29
Robust language pair-independent sub-tree alignment
Tinsley, J. F., Zhechev, V., Hearne, M., & Way, A. (2007) · Arrow@dit (Dublin Institute of Technology) · Cited by 29
Exploiting Neural Query Translation into Cross Lingual Information RetrievalPreprint
Yao, L., Yang, B., Zhang, H., Luo, W., & Chen, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13659 · Cited by 34
“Moses: Open source toolkit for statistical machine translation.”
InfluentialWord Alignment with Stochastic Bracketing Linear Inversion Transduction Grammar
Saers, M., Nivre, J., & Wu, D. (2010) · Cited by 28
Using a Grammar Checker for Evaluation and Postprocessing of Statistical Machine Translation
Stymne, S., & Ahrenberg, L. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/27f7h5poasoc · Cited by 28
Paraphrase Lattice for Statistical Machine Translation
Onishi, T., Utiyama, M., & Sumita, E. (2010) · Cited by 28
Automatic Building and Using Parallel Resources for SMT from Comparable Corpora
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Online Learning for Statistical Machine Translation
Ortiz-Martínez, D. (2016) · Computational Linguistics · doi:10.1162/coli_a_00244 · Cited by 28
Maintaining Sentiment Polarity in Translation of User-Generated Content
Lohar, P., Afli, H., & Way, A. (2017) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2017-0010 · Cited by 28
Statistical machine translation
Babhulgaonkar, A. R., & Bharad, S. V. (2017) · International Conference on Intelligent Systems and Information Management (ICISIM) · doi:10.1109/icisim.2017.8122149 · Cited by 28
Haque, R., Penkale, S., & Way, A. (2018) · Language Resources and Evaluation · doi:10.1007/s10579-018-9412-4 · Cited by 28
“In order to perform tokenisation for English, Hindi and Turkish, we used the standard tokenisation tool 10 of the Moses toolkit (Koehn et al. 2007).”
MethodMultimodal Lexical Translation
Lala, C., & Specia, L. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/376c44dt48af · Cited by 28
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
Bansal, S., Kamper, H., Livescu, K., Lopez, A., & Goldwater, S. J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1006 · Cited by 58
Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
Napoles, C., Nadejde, M., & Tetreault, J. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00282 · Cited by 45
Phrasal: A Statistical Machine Translation Toolkit for Exploring New Model Features
Cer, D. M., Galley, M., Jurafsky, D. S., & Manning, C. D. (2010) · North American Chapter of the Association for Computational Linguistics · Cited by 28
Analysing terminology translation errors in statistical and neural machine translation
Haque, R., Hasanuzzaman, M., & Way, A. (2020) · Machine Translation · doi:10.1007/s10590-020-09251-z · Cited by 28
“To build our PB-SMT systems we used the Moses toolkit.”
MethodInfluentialGHKM Rule Extraction and Scope-3 Parsing in Moses
Williams, P. J., & Koehn, P. (2012) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 28
Self-citationA Factory of Comparable Corpora from Wikipedia
Barrón‐Cedeño, A., España-Bonet, C., Trapote, J. B., & Màrquez, L. (2015) · Workshop on Building and Using Comparable Corpora (BUCC) · doi:10.18653/v1/w15-3402 · Cited by 28
Analyzing Error Types in English-Czech Machine Translation
Bojar, O. (2011) · The Prague Bulletin of Mathematical Linguistics · doi:10.2478/v10108-011-0005-2 · Cited by 28
Self-citationRama, T., Singh, A. K., & Kolachina, S. (2009) · doi:10.3115/1620932.1620948 · Cited by 28
The role of pseudo references in MT evaluation
Albrecht, J. S., & Hwa, R. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626424 · Cited by 28
DCEP -Digital Corpus of the European Parliament
Hajlaoui, N., Kolovratník, D., Väyrynen, J. J., Steinberger, R., & Varga, D. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/43yfy54y6qmi · Cited by 28
Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT
Lambert, P. (2015) · doi:10.3115/v1/p15-2128 · Cited by 28
Omorfi —Free and open source morphological lexical database for Finnish
Pirinen, T. A. (2015) · DSpace repository (University of Tartu) · Cited by 28
OCR Error Correction: State-of-the-Art vs an NMT-based Approach
Mokhtar, K., Bukhari, S. S., & Dengel, A. R. (2018) · IAPR International Workshop on Document Analysis Systems (DAS) · doi:10.1109/das.2018.63 · Cited by 30
“The used approaches are as follows: • state-of-the-art approaches – Uni-gram language modeling [7] (Uni-gram) – Statistical Machine Translation [8] (SMT) • introduced novel approaches – Word based model (WBM) – Character based model (CBM) – Word based model with Normalization (WBM++) – Character based model with Norma…”
BackgroundInfluentialFrom Bilingual to Multilingual Neural Machine Translation by Incremental Training
Escolano, C., Costa‐jussà, M. R., & Fonollosa, J. A. R. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2033 · Cited by 29
“These steps were performed using the scripts available from Moses (Koehn et al., 2007).”
MethodPhrase-based and deep syntactic English-to-Czech statistical machine translation
Bojar, O., & Hajič, J. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626413 · Cited by 28
Self-citationLearning Semantic Representations for the Phrase Translation ModelPreprint
Gao, J., He, X., Yih, W.-T., & Deng, L. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.0482 · Cited by 28
Active learning and crowdsourcing for machine translation in low resource scenarios
Carbonell, J., & Ambati, V. (2012) · Cited by 27
Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine TranslationPreprint
Marie, B., & Fujita, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12703 · Cited by 28
Multilingual Real-time Event Extraction for Border Security Intelligence Gathering
Atkinson, M., Jakub Piskorski, B., van der Goot, E., & Yangarber, R. (2011) · Lecture notes in social networks · doi:10.1007/978-3-7091-0388-3_18 · Cited by 27
Statistical machine translation enhancements through linguistic levels
Costa‐jussà, M. R., & Farrús, M. (2014) · ACM Computing Surveys · doi:10.1145/2518130 · Cited by 27
Incremental syntactic language models for phrase-based translation
Schwartz, L., Callison-Burch, C., Schuler, W., & Wu, S. (2011) · Cited by 27
Self-citationTowards Using Web-Crawled Data for Domain Adaptation in Statistical Machine Translation
Pecina, P., Toral, A., Way, A., Papavassiliou, V., Prokopidis, P., & Γιάγκου, Μ. (2011) · Arrow@dit (Dublin Institute of Technology) · Cited by 27
Sparse Bilingual Word Representations for Cross-lingual Lexical Entailment
Vyas, Y., & Carpuat, M. J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1142 · Cited by 27
Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression.
Blevins, T., Kwiatkowski, R., Macbeth, J., McKeown, K. R., Patton, D. U., & Rambow, O. (2016) · Smith ScholarWorks (Smith College) · Cited by 26
Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Salesky, E., Sperber, M., & Waibel, A. H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1285 · Cited by 29
“pl from the Moses toolkit (Koehn et al., 2007).”
Unsupervised Clinical Language Translation
Weng, W., Chung, Y.-A., & Peter Szolovits, B. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330710 · Cited by 27
“We adopted Moses, a widely-used statistical MT engine that is used to train statistical translation models [24].”
MethodWAT2019: English-Hindi Translation on Hindi Visual Genome Dataset
Meetei, L. S., Singh, T. D., & Bandyopadhyay, S. (2019) · Workshop on Asian Translation (WAT) · doi:10.18653/v1/d19-5224 · Cited by 27
“The author also evaluated the impact of adding synthetic multi-modal, multilingual data generated us-ing phrase-based statistical machine translation(PBSMT) trained on the dataset from Multi30k (Elliott et al., 2016).”
MethodInfluentialA Review of Shorthand Systems: From Brachygraphy to Microtext and Beyond
Satapathy, R., Cambria, E., Nanetti, A., & Hussain, A. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09723-7 · Cited by 28
“The tool that was used to build this system is Moses [40].”
MethodTeixeira, C. S. C. (2014) · Conference of the Association for Machine Translation in the Americas · Cited by 27
Vanmassenhove, E., Emmery, C., & Shterionov, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.704 · Cited by 35
“All data is preprocessed using the Moses (de)tokenizer (Koehn et al., 2007).”
MethodAutomated transpilation of imperative to functional code using neural-guided program synthesis
Mariano, B., Chen, Y., Feng, Y., Durrett, G., & Dillig, I. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3527315 · Cited by 31
“Another emerging approach to transpilation is based on machine translation, and such techniques have been used to translate from Java to C# [Koehn et al. 2007], Python2 to Python3 [Aggarwal et al. 2015], and CoffeScript to JavaScript [Chen et al. 2018].”
MethodConsensus training for consensus decoding in machine translation
Pauls, A., DeNero, J., & Klein, D. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699648.1699688 · Cited by 27
How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models
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Translating Literary Text between Related Languages using SMT
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BLEU deconstructed: Designing a Better MT Evaluation Metric
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Normalization of text messages using character- and phone-based machine translation approaches
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Persian–Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language
Ahmadnia, B., Serrano, J., & Haffari, G. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_004 · Cited by 27
“MOSES package 2 (Koehn et al., 2007), is employed for training our SMT systems.”
Methodvan der Lee, C., Krahmer, E., & Wubben, S. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6504 · Cited by 25
Controlling the Reading Level of Machine Translation Output
Marchisio, K., Guo, J., Lai, C.-I., & Koehn, P. (2019) · Cited by 27
Self-citationMulti-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
Jiang, H., Chen, L., Wang, C., & Zhao, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.165 · Cited by 28
Statistical machine translation for biomedical text: are we there yet?
Wu, C., Xia, F., Deléger, L., & Solti, I. (2011) · PubMed Central · Cited by 27
Effects of morphological analysis in translation between German and English
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Domain and Dialect Adaptation for Machine Translation into Egyptian Arabic
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Combining Contextualized and Non-contextualized Query Translations to Improve CLIR
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“The parallel data is tokenized using Moses [7] toolkit, lowercased and preprocessed to strip any punctuation, digits, and accents from the characters.”
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Lattice-based system combination for statistical machine translation
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Improving the Cross-Lingual Projection of Syntactic Dependencies
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Complex Word Identification Based on Frequency in a Learner Corpus
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Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
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“After tokenization and true-casing using Moses toolkit (Koehn et al., 2007), byte pair encoding (BPE) (Sennrich et al., 2016) is used jointly with 20k merge operations.”
Xu, Q., Qu, L., Xu, C., & Cui, R. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8633 · Cited by 48
Lexically Constrained Neural Machine Translation with Explicit Alignment Guidance
Chen, G., Chen, Y., & Li, V. O. K. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17496 · Cited by 26
“All texts are tokenized using the Moses tokenizer (Koehn et al. 2007).”
MethodCross-Language Frame Semantics Transfer in Bilingual Corpora
Basili, R., Cao, D. D., Croce, D., Coppola, B., & Moschitti, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00382-0_27 · Cited by 26
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Taking Statistical Machine Translation to the Student Translator
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Building a Dataset of Multilingual Cognates for the Romanian Lexicon
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Topic-Informed Neural Machine Translation
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Neural Lattice Search for Domain Adaptation in Machine Translation
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Self-citationCoupling Statistical Machine Translation with Rule-based Transfer and Generation.
Ahsan, A., Kolachina, P., Kolachina, S., Sharma, D. M., & Sangal, R. (2010) · Conference of the Association for Machine Translation in the Americas · Cited by 26
Zhechev, V., & van Genabith, J. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 26
Improving word alignment using syntactic dependencies
Ma, Y., Ozdowska, S., Sun, Y., & Way, A. (2008) · Workshop on Syntax and Structure in Statistical Translation (SSST) · doi:10.3115/1626269.1626278 · Cited by 26
Spelling Normalisation and Linguistic Analysis of Historical Text for Information Extraction
Pettersson, E. (2016) · Uppsala University Publications (Uppsala University) · Cited by 26
Neighbors helping the poor: improving low-resource machine translation using related languages
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Hybrid rule-based - example-based MT: feeding Apertium with sub-sentential translation units
Sánchez-Martínez, F., Forcada, M. L., & Way, A. (2009) · Arrow@dit (Dublin Institute of Technology) · Cited by 25
Adaptive Quality Estimation for Machine Translation
Turchi, M., Anastasopoulos, A., de Souza, J. G. C., & Negri, M. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1067 · Cited by 25
Minimum Translation Modeling with Recurrent Neural Networks
Hu, Y., Auli, M., Gao, Q., & Gao, J. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1003 · Cited by 25
Kneser-Ney Smoothing on Expected Counts
Zhang, H., & Chiang, D. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1072 · Cited by 25
On the Elements of an Accurate Tree-to-String Machine Translation System
Neubig, G., & Duh, K. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2024 · Cited by 25
Normalising orthographic and dialectal variants for the automatic processing of Swiss German
Samardżić, T., Scherrer, Y., & Glaser, E. (2015) · Archive ouverte UNIGE (University of Geneva) · doi:10.5167/uzh-126461 · Cited by 25
First Steps Towards Coverage-Based Document Alignment
Gomes, L., & Lopes, G. P. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2369 · Cited by 25
The Regression Model of Machine Translation
Biçici, M. E. (2012) · Cited by 25
Corpus Creation and Initial SMT Experiments between Spanish and Shipibo-konibo
Grupo de Reconocimiento de Patrones e Inteligencia Artificial Aplicada Pontificia Universidad Católica del Perú, L., Galarreta, A.-P., Melgar, A., & Oncevay, A. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_033 · Cited by 25
“Then, we trained the SMT using the MOSES platform (Koehn et al., 2007).”
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
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