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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
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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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SMT versus NMT: Preliminary comparisons for Irish
Dowling, M., Lynn, T., Poncelas, A., & Way, A. (2018) · Arrow@dit (Dublin Institute of Technology) · Cited by 25
Multi-Source Syntactic Neural Machine Translation
Currey, A., & Heafield, K. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1327 · Cited by 25
“We tokenize and truecase the data using the Moses tokenizer and truecaser (Koehn et al., 2007).”
MethodRevisiting Low-Resource Neural Machine Translation: A Case Study
Sennrich, R., & Zhang, B. (2019) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.1905.11901 · Cited by 25
“We use Moses (Koehn et al., 2007) to train a PBSMT system.”
MethodA Discriminative Neural Model for Cross-Lingual Word Alignment
Stengel-Eskin, E., Su, T.-R., Post, M., & Durme, B. V. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1084 · Cited by 24
Multilingual Multimodal Machine Translation for Dravidian Languages utilizing Phonetic Transcription
Chakravarthi, B. R., Priyadharshini, R., Stearns, B., Jayapal, A., Sridevy, S., Arčan, M., Zarrouk, M., & McCrae, J. P. (2019) · Arrow@dit (Dublin Institute of Technology) · doi:10.13025/21385 · Cited by 25
Language Identification for Multilingual Machine Translation
Babhulgaonkar, A. R., & Sonavane, S. P. (2020) · doi:10.1109/iccsp48568.2020.9182184 · Cited by 25
“Phrase based machine translation systems are developed using MOSES decoder [5].”
MethodA Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning
Xu, C., Wang, J., Tang, Y., Guzmán, F., Rubinstein, B. I. P., & Cohn, T. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450034 · Cited by 31
“processing steps: each sentence is tokenised with the Moses tokeniser [22], Byte-Pair-Encoding [39] is applied to each dataset”
MethodDeep Transformer modeling via grouping skip connection for neural machine translation
Li, Y., Li, J., & Zhang, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107556 · Cited by 26
A survey on syntactic processing techniques
Zhang, X., Mao, R., & Cambria, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10300-7 · Cited by 27
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation
Yang, J., Yin, Y., Yang, L., Ma, S., Huang, H., Zhang, D., Wei, F., & Li, Z. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3221040 · Cited by 27
“All language pairs are tokenized by Moses [21] and jointly byte pair encoded (BPE) [22] with 40K merge operations using a shared vocabulary.”
BackgroundInfluentialLocally Training the Log-Linear Model for SMT
Liu, L., Cao, H., Watanabe, T., Zhao, T., Yu, M., & Zhu, C. (2012) · Cited by 25
A systematic analysis of translation model search spaces
Auli, M., Lopez, A., Hoang, H., & Koehn, P. (2009) · Conference on Machine Translation (WMT) · doi:10.3115/1626431.1626475 · Cited by 25
Self-citationSemantic spaces for improving language modeling
Brychcín, T., & Konopík, M. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.05.001 · Cited by 25
Li, M., Duan, N., Zhang, D., Li, C.-H., & Zhou, M. Q. (2009) · doi:10.3115/1690219.1690228 · Cited by 25
Storing the Web in Memory: Space Efficient Language Models with Constant Time Retrieval
Guthrie, D., & Hepple, M. (2010) · Cited by 25
Context-aware correction of spelling errors in Hungarian medical documents
Siklósi, B., Novák, A., & Prószéky, G. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.09.001 · Cited by 25
Discriminative Modeling of Extraction Sets for Machine Translation
DeNero, J., & Klein, D. (2010) · Cited by 25
LIMSI's statistical translation systems for WMT'08
Déchelotte, D., Adda, G., Allauzen, A., Bonneau-Maynard, H., Galibert, O., Gauvain, J., Langlais, P., & Yvon, F. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626404 · Cited by 25
Banerjee, P., Naskar, S. K., Roturier, J., Way, A., & van Genabith, J. (2012) · Cited by 25
Henríquez, C. A., & Hern, A. (2009) · Cited by 25
Lu, B., Jiang, T., Chow, K.-P., & Tsou, B. K. (2010) · Cited by 25
Kouremenos, D., Ntalianis, K., & Kollias, S. (2018) · Computer Speech & Language · doi:10.1016/j.csl.2018.04.001 · Cited by 25
Target Foresight Based Attention for Neural Machine Translation
Li, X., Liu, L., Tu, Z., Shi, S., & Meng, M. Q. ‐. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1125 · Cited by 25
“• Moses (Koehn et al., 2007): an open source phrased based translation system with default configuration.”
BackgroundMethodHierarchical Transfer Learning Architecture for Low-Resource Neural Machine Translation
Luo, G., Yang, Y., Yuan, Y., Chen, Z., & Ainiwaer, A. (2019) · IEEE Access · doi:10.1109/access.2019.2936002 · Cited by 25
“The first baseline is the Phrase-based SMT system that is based on Moses [9].”
MethodInfluentialThe (Un)faithful Machine Translator
Jones, R., & Irvine, A. (2013) · Cited by 25
Optimal Bilingual Data for French-English PB-SMT.
Ozdowska, S., & Way, A. (2009) · Arrow@dit (Dublin Institute of Technology) · Cited by 25
Eisner, J. M., & Dreyer, M. (2011) · Cited by 23
Iterative Refinement for Machine TranslationPreprint
Novak, R., Auli, M., & Grangier, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.06602 · Cited by 25
Machine Translation Approaches and Survey for Indian LanguagesPreprint
Khan, N. J., Anwar, W., & Durrani, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.04290 · Cited by 25
“a. Next, training, tuning and test sets are tokenized for all folds. Finally, all datasets are converted to lowercase. This process is repeated for all language pairs using scripts provided by Moses (Koehn et al. 2007) decoder. The lowercase training data is used for word alignment. Baseline Settings: We trained a Mos…”
MethodInfluentialDabre, R., Cromierès, F., & Kurohashi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.06135 · Cited by 25
Imankulova, A., Dabre, R., Fujita, A., & Imamura, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03060 · Cited by 27
“ion system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) (Koehn et al., 2007). Although NMT can be significantly better than PBSMT in resourcerich scenarios, PBSMT performs bette…”
BackgroundMethodInfluentialInducing Synchronous Grammars with Slice Sampling
Blunsom, P., & Cohn, T. (2010) · Cited by 24
Zhang, J., & Zong, C. (2015) · Cited by 24
PORT: a Precision-Order-Recall MT Evaluation Metric for Tuning
Chen, B., Kühn, R., & Larkin, S. (2012) · NPARC · Cited by 24
LIG System for Word Level QE task at WMT14
Luong, N.-Q., Besacier, L., & Lecouteux, B. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3342 · Cited by 24
Automatic Construction of Discourse Corpora for Dialogue Translation
Wang, L., Zhang, X., Tu, Z., Way, A., & Liu, Q. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2tqworp5zxp9 · Cited by 24
Continuous Space Language Models using Restricted Boltzmann Machines
Niehues, J., & Waibel, A. (2012) · KITopen · doi:10.5445/ir/1000145058 · Cited by 24
Ambient intelligence framework for real-time speech-to-sign translation
Otoom, M., & Alzubaidi, M. A. (2017) · Assistive Technology · doi:10.1080/10400435.2016.1268218 · Cited by 24
“Specifically, the decoder consists of the following four modules: (1) Input: Accepts the source sentence to be translated; (2) Translational model: Represented as a set of rules, and can be supported with features to add information that enhance the translation process; (3) Language model: Moses supports several model…”
InfluentialOCR Error Correction Using Statistical Machine Translation
Afli, H., Barrault, L., & Schwenk, H. (2015) · HAL · Cited by 24
Using Images to Improve Machine-Translating E-Commerce Product Listings.
Calixto, I., Stein, D., Matusov, E., Lohar, P., Castilho, S., & Way, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2101 · Cited by 24
“We use a PBSMT model built with the Moses SMT Toolkit (Koehn et al., 2007).”
MethodShivakumar, P. G., Li, H., Knight, K., & Georgiou, P. (2019) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2018.31 · Cited by 24
Neural Hidden Markov Model for Machine Translation
Wang, W., Zhu, D., Alkhouli, T., Gan, Z., & Ney, H. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2060 · Cited by 24
An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation
Ataman, D., & Federico, M. (2018) · Conference of the Association for Machine Translation in the Americas · Cited by 24
Self-citationAdaptive Edit-Distance and Regression Approach for Post-OCR Text Correction
Nguyen, T.-T.-H., Coustaty, M., Doucet, A., Jatowt, A., & Nguyen, N.-V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04257-8_29 · Cited by 25
Designing High Accuracy Statistical Machine Translation for Sign Language Using Parallel Corpus
Othman, A., & Jemni, M. (2019) · Journal of Information Technology Research · doi:10.4018/jitr.2019040108 · Cited by 24
Statistical Significance Testing for Natural Language Processing
Dror, R., Peled-Cohen, L., Shlomov, S., & Reichart, R. (2020) · Synthesis lectures on human language technologies · doi:10.2200/s00994ed1v01y202002hlt045 · Cited by 55
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
Zhang, S., Frey, B. E., & Bansal, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.43 · Cited by 28
Fine-tuning neural machine translation on gender-balanced datasets
Costa‐jussà, M. R., & Sánchez, A. D. J. (2020) · Cited by 24
Detailed Analysis of different Strategies for Phrase Table Adaptation in SMT
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Tsvetkov, Y., Metze, F., & Dyer, C. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1065 · Cited by 24
Self-citationModelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation
Sennrich, R. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00131 · Cited by 24
A hybrid machine translation architecture guided by syntax
Labaka, G., España-Bonet, C., Màrquez, L., & Sarasola, K. (2014) · Machine Translation · doi:10.1007/s10590-014-9153-0 · Cited by 24
Lagarda, A. L., Ortiz-Martínez, D., Alabau, V., & Casacuberta, F. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.10.004 · Cited by 24
Online learning approaches for Computer Assisted Translation
Mathur, P. (2013) · doi:10.13140/2.1.3129.0886 · Cited by 25
Mining a comparable text corpus for a Vietnamese - French statistical machine translation system
Do, T.-N.-D., Le, V.-B., Bigi, B., Besacier, L., & Castelli, E. (2009) · Conference on Machine Translation (WMT) · doi:10.3115/1626431.1626466 · Cited by 24
Monte carlo inference and maximization for phrase-based translation
Arun, A., Dyer, C., Haddow, B., Blunsom, P., Lopez, A., & Koehn, P. (2009) · Conference on Computational Natural Language Learning · doi:10.3115/1596374.1596394 · Cited by 24
Self-citationYepes, A. J., Prieur-Gaston, É., & Aurélie Névéol, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-146 · Cited by 24
TransSearch: from a bilingual concordancer to a translation finder
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Manawi: Using Multi-Word Expressions and Named Entities to Improve Machine Translation
Tan, L., & Pal, S. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3323 · Cited by 24
On the Evaluation of Adaptive Machine Translation for Human Post-Editing
Bentivogli, L., Bertoldi, N., Cettolo, M., Federico, M., Negri, M., & Turchi, M. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2509241 · Cited by 24
Self-citationAutomatic Detection of Machine Translated Text and Translation Quality Estimation
Aharoni, R., Koppel, M., & Goldberg, Y. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2048 · Cited by 26
Syntax-based Rewriting for Simultaneous Machine Translation
He, H., Grissom, A., Morgan, J., Boyd-Graber, J. L., & Daumé, H. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1006 · Cited by 24
Corpus Expansion for Statistical Machine Translation with Semantic Role Label Substitution Rules
Gao, Q., & Vogel, S. (2011) · Cited by 24
Singh, T. D., & Bandyopadhyay, S. (2010) · Cited by 24
Mining parallel fragments from comparable texts.
Cettolo, M., Federico, M., & Bertoldi, N. (2010) · IWSLT · Cited by 24
Self-citationPre-Reordering for Neural Machine Translation: Helpful or Harmful?
Du, J., & Way, A. (2017) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2017-0018 · Cited by 24
Machine Translation Quality Estimation: Applications and Future Perspectives
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Utilizing Knowledge Graphs for Neural Machine Translation Augmentation
Moussallem, D., Ngomo, A.-C. N., Buitelaar, P., & Arčan, M. (2019) · International Conference on Knowledge Capture · doi:10.1145/3360901.3364423 · Cited by 29
From bilingual to multilingual neural‐based machine translation by incremental training
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“These steps were performed using the scripts available from the Moses tools (Koehn et al., 2007).”
Evaluation of MT Systems to Translate User Generated Content
Roturier, J., & Bensadoun, A. (2011) · Cited by 24
Zero Pronoun Resolution can Improve the Quality of J-E Translation
Taira, H., Sudoh, K., & Nagata, M. (2012) · Cited by 24
González-Rubio, J., Ortiz-Martínez, D., & Casacuberta, F. (2010) · RiuNet (Universitat Politècnica de València) · Cited by 24
A GRU-Gated Attention Model for Neural Machine TranslationPreprint
Zhang, B., Xiong, D., & Su, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.08430 · Cited by 33
“We compared our proposed model against the following two state-of-the-art SMT and NMT systems: • Moses [Koehn et al. , 2007]: an open source state-of-the-art phrase-based SMT system.”
MethodInfluentialBelinkov, Y. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 24
ConvLab: Multi-Domain End-to-End Dialog System PlatformPreprint
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“For example, among many, Moses (Koehn et al., 2007), HTK (Young et al., 2002) and CoreNLP (Manning et al., 2014) have been widely used to facilitate subsequent research in machine translation, speech recognition and natural language processing, respectively.”
BackgroundNon-autoregressive Transformer by Position LearningPreprint
Bao, Y., Zhou, H., Feng, J., Wang, M., Huang, S., Chen, J., & Li, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10677 · Cited by 30
“tion We valid the effectiveness of PNAT on the most widely used benchmarks for machine translation — WMT14 EN-DE(4.5M pairs) and IWSLT16 DE-EN(196K pairs). The dataset is processed with Moses script (Koehn et al., 2007), and the words are segmented into subword units using byte-pair encoding (Sennrich et al., 2016, BP…”
MethodA Bayesian non-linear method for feature selection in machine translation quality estimation
Shah, K., Cohn, T., & Specia, L. (2015) · Machine Translation · doi:10.1007/s10590-014-9164-x · Cited by 23
Distinguishing Degrees of Compositionality in Compound Splitting for Statistical Machine Translation
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Inducing Discourse Connectives from Parallel Texts
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Alibaba’s Neural Machine Translation Systems for WMT18
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Unsupervised Statistical Text Simplification
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“Our model utilizes a phrase-based machine translation (PBMT) system [9] as the underlying back-bone model.”
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Unpacking and Transforming Feature Functions: New Ways to Smooth Phrase Tables
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Modeling northern and southern varieties of dutch for STT
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Hybrid machine translation guided by a rule-based system
España-Bonet, C., Labaka, G., de Ilarraza, A. D., & Màrquez, L. (2011) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 23
Syntax-Based Word Ordering Incorporating a Large-Scale Language Model
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Extracting Pre-ordering Rules from Predicate-Argument Structures
Wu, X., Sudoh, K., Duh, K., Tsukada, H., & Nagata, M. (2011) · Cited by 23
Mining and modeling relations between formal and informal Chinese phrases from web corpora
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Multilingual Speech-to-Speech Translation System: VoiceTra
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Crowdsourcing High-Quality Parallel Data Extraction from Twitter
Ling, W., Marujo, L., Dyer, C., Black, A. W., & Trancoso, I. M. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3356 · Cited by 23
Self-citationData cleaning for word alignment
Okita, T. (2009) · doi:10.3115/1667884.1667895 · Cited by 23
RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translation
Wang, Y., Wang, Y., Wang, S., Guo, D., Chen, J., Grundy, J., Liu, X., Ma, Y., Mao, M., Zhang, H., et al. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3645056 · Cited by 12
“Aggarwal et al. [10] convert Python 2 code to Python 3 code using trained Moses [54], which is an open-source toolkit for statistical machine translation.”
Mining the Correlation between Human and Automatic Evaluation at Sentence Level
Sun, Y. (2010) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/2cpgtwtr2gg4 · Cited by 6
(2011) · Cited by 446
Deep Learning Scaling is Predictable, Empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M. M. A., Yang, Y., & Zhou, Y. (2017) · arXiv · doi:10.48550/arxiv.1712.00409 · Cited by 427
“We clean and tokenize the data set using Moses (Koehn et al. (2007)) as described by Luong et al. (2017).”
MethodAre Sixteen Heads Really Better than One?
Michel, P., Levy, O., & Neubig, G. (2019) · arXiv · doi:10.48550/arxiv.1905.10650 · Cited by 404
“pretrained model of Ott et al. 2018.3 and report BLEU scores on the newstest2013 test set. In accordance with Ott et al. 2018, we compute BLEU scores on the tokenized output of the model using Moses (Koehn et al., 2007). Statistical significance is tested with paired bootstrap resampling (Koehn, 2004) using compare-mt4…”
MethodInfluentialBenchmarking Model-Based Reinforcement Learning
Wang, T., Bao, X., Clavera, I., Hoang, J., Wen, Y., Langlois, E., Zhang, S., Zhang, G., Abbeel, P., & Ba, J. (2019) · arXiv · doi:10.48550/arxiv.1907.02057 · Cited by 226
“[26] Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al.”
BackgroundMethod(2012) · Cited by 163
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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