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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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The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation
Durrani, N., Schmid, H., Fraser, A., Koehn, P., & Schütze, H. (2015) · Computational Linguistics · doi:10.1162/coli_a_00218 · Cited by 39
Self-citationChollampatt, S., Hoang, D. T., & Ng, H. T. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1195 · Cited by 39
Pinnis, M., Krišlauks, R., Deksne, D., & Miks, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64206-2_27 · Cited by 39
Translation Quality and Productivity: A Study on Rich Morphology Languages.
Specia, L., Harris, K. R., Burchardt, A., Turchi, M., Negri, M., & Skadiņa, I. (2017) · Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton) · Cited by 39
Generalizing Back-Translation in Neural Machine Translation
Graça, M., Kim, Y., Schamper, J., Khadivi, S., & Ney, H. (2019) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w19-5205 · Cited by 39
“Case-sensitive B LEU (Papineni et al., 2002) is computed using the mteval 13a.pl script from Moses (Koehn et al., 2007).”
MethodLinguistically Motivated Parallel Data Augmentation for Code-Switch Language Modeling
Lee, G., Yue, X., & Li, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1382 · Cited by 39
“The phrase alignment and phrase extraction can be achieved using a statistical machine translation system, such as Moses [22].”
MethodCross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning
Fei, H., & Li, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.510 · Cited by 39
“Following Lample et al. (2018c), we extract and tokenize monolingual data of each language using Moses (Koehn et al., 2007).”
MethodQuality evaluation in community post-editing
Mitchell, L., O’Brien, S., & Roturier, J. (2014) · Machine Translation · doi:10.1007/s10590-014-9160-1 · Cited by 38
OxLM: A Neural Language Modelling Framework for Machine Translation
Baltescu, P., Blunsom, P., & Hoang, H. (2014) · The Prague Bulletin of Mathematical Linguistics · doi:10.2478/pralin-2014-0016 · Cited by 38
Self-citationA Parallel Corpus of Arabic-Japanese News Articles
Inoue, G., Habash, N. Y., Matsumoto, Y., & Aoyama, H. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4ekd22eqevn6 · Cited by 38
“Phrase-based MT Settings We use the Moses toolkit (Koehn et al., 2007) to build a standard phrase-based MT system.”
MethodEfficient Incremental Decoding for Tree-to-String Translation
Huang, L., & Mi, H. (2010) · Cited by 38
Phrasal: A Toolkit for New Directions in Statistical Machine Translation
Green, S., Cer, D. M., & Manning, C. D. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3311 · Cited by 38
Smooth Bilingual N-gram Translation
Schwenk, H., Costa‐jussà, M. R., & Fonollosa, J. A. R. (2007) · Cited by 38
Inferring Shallow-Transfer Machine Translation Rules from Small Parallel Corpora
Sánchez-Martínez, F., & Forcada, M. L. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2735 · Cited by 38
Exploiting Parallel Treebanks to Improve Phrase-Based Statistical Machine Translation
Tinsley, J. F., Hearne, M., & Way, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00382-0_26 · Cited by 38
A Multi-Domain Translation Model Framework for Statistical Machine Translation
Sennrich, R., Schwenk, H., & Aransa, W. (2013) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-79616 · Cited by 38
Grammatical and context‐sensitive error correction using a statistical machine translation framework
Ehsan, N., & Faili, H. (2012) · Software Practice and Experience · doi:10.1002/spe.2110 · Cited by 38
Jane 2: Open Source Phrase-based and Hierarchical Statistical Machine Translation
Wuebker, J., Huck, M., Peitz, S., Nuhn, M., Freitag, M., Peter, J.-T., Mansour, S., & Ney, H. (2012) · RWTH Publications (RWTH Aachen) · Cited by 38
A Deeper Exploration of the Standard PB-SMT Approach to Text Simplification and its Evaluation
Štajner, S., Béchara, H., & Saggion, H. (2015) · doi:10.3115/v1/p15-2135 · Cited by 38
A Novel Approach to Dropped Pronoun Translation
Wang, L., Tu, Z., Zhang, X., Li, H., Way, A., & Liu, Q. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1113 · Cited by 38
Large and Diverse Language Models for Statistical Machine Translation
Schwenk, H., & Koehn, P. (2008) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 38
Self-citationEvaluating prose style transfer with the Bible
Carlson, K., Riddell, A. B., & Rockmore, D. N. (2018) · Royal Society Open Science · doi:10.1098/rsos.171920 · Cited by 52
“The statistical machine translation system Moses [23] is an established baseline for testing new paraphrasing corpora and models.”
Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process
Macken, L., Prou, D., & Tezcan, A. (2020) · Informatics · doi:10.3390/informatics7020012 · Cited by 38
“A phrase-based statistical machine translation (PBSMT) system based on Moses [3], was introduced in 2013.”
MethodNeural machine translation with a polysynthetic low resource language
Ortega, J. E., Mamani, R. A. C., & Cho, K. (2020) · Machine Translation · doi:10.1007/s10590-020-09255-9 · Cited by 42
“We experiment with two main neural architectural models: the LSTM model (Bahdanau et al. 2014; Sutskever et al. 2014; Hochreiter and Schmidhuber 1997) The corpora were not modified apart from normalization of Quechua text using a morhphological normalizer Rios and Castro-Mamani (2014) and tokenization and truecasing u…”
MethodInfluentialIncremental Re-training for Post-editing SMT
Hardt, D., & Elming, J. (2010) · CBS Research Portal (Copenhagen Business School) · Cited by 38
Evaluating MT Adequacy without Reference Translations
Mehdad, Y., Negri, M., & Federico, M. (2012) · Cited by 39
Self-citationExtracting Parallel Phrases from Comparable Data
Hewavitharana, S., & Vogel, S. (2013) · Springer eBooks · doi:10.1007/978-3-642-20128-8_10 · Cited by 38
Cognate Production using Character-based Machine Translation
Beinborn, L., Zesch, T., & Gurevych, I. (2013) · TUbilio (Technical University of Darmstadt) · doi:10.17185/duepublico/72180 · Cited by 38
Simple, lexicalized choice of translation timing for simultaneous speech translation
Fujita, T., Neubig, G., Sakti, S., Toda, T., & Nakamura, S. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-615 · Cited by 38
Bridging Neural Machine Translation and Bilingual DictionariesPreprint
Zhang, J., & Zong, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07272 · Cited by 58
The first Automatic Translation Memory Cleaning Shared Task
Barbu, E., Escartín, C. P., Bentivogli, L., Negri, M., Turchi, M., Orǎsan, C., & Federico, M. (2016) · Machine Translation · doi:10.1007/s10590-016-9183-x · Cited by 37
Self-citationReport of NEWS 2018 Named Entity Transliteration Shared Task
Chen, N. F., Banchs, R. E., Zhang, M., Duan, X., & Li, H. (2018) · doi:10.18653/v1/w18-2409 · Cited by 37
The SDL Language Weaver Systems in the WMT12 Quality Estimation Shared Task
Soricut, R., Bách, N., & Wang, Z. (2012) · Cited by 37
Salton, G. D., Ross, R., & Kelleher, J. D. (2014) · doi:10.3115/v1/w14-1007 · Cited by 37
Correlations between productivity and quality when post-editing in a professional context
Arenas, A. G. (2014) · Machine Translation · doi:10.1007/s10590-014-9155-y · Cited by 37
Multilingual Annotation and Disambiguation of Discourse Connectives for Machine Translation
Zufferey, S., Popescu-Belis, A., Cartoni, B., & Meyer, T. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.7892/boris.78682 · Cited by 37
Wołk, K., & Marasek, K. P. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.456 · Cited by 37
Multi-modular domain-tailored OCR post-correction
Schulz, S., & Kuhn, J. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1288 · Cited by 37
“We train four different systems: • token level – domain specific data (cf. Section 4.1) – general data (cf. Section 4.2) • character level – domain specific data (cf. Section 4.1) – general data (cf. Section 4.2) The models are trained with the Moses toolkit (Koehn et al., 2007).”
MethodInfluentialHow Much Does Tokenization Affect Neural Machine Translation?
Domingo, M., García-Martínez, M., Helle, A., Casacuberta, F., & García–Herranz, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-24337-0_38 · Cited by 63
Factored Neural Machine Translation Architectures
García-Martínez, M., Barrault, L., & Bougares, F. (2016) · HAL · Cited by 37
Collecting and Using Comparable Corpora for Statistical Machine Translation
Skadiņa, I., Aker, A., Mastropavlos, N., Su, F., Tufis, D. I., Verlič, M., Vasiļjevs, A., Babych, B., Clough, P. D., Gaizauskas, R., et al. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2n89bqp42nan · Cited by 38
Coarse-to-fine syntactic machine translation using language projections
Petrov, S., Haghighi, A., & Klein, D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613731 · Cited by 37
Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
Limsopatham, N., & Collier, N. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1194 · Cited by 36
Design and realization of a modular architecture for textual entailment
Padó, S., Noh, T.-G., Stern, A., Wang, R., & Zanoli, R. (2013) · Natural Language Engineering · doi:10.1017/s1351324913000351 · Cited by 36
Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction
Clifton, A., & Sarkar, A. (2011) · Cited by 36
Free tools and resources for Brazilian Portuguese speech recognition
Neto, N., Patrick, C., Junior, A. B. D. R. K., & Trancoso, I. M. (2010) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-010-0023-1 · Cited by 36
Improving Text Normalization via Unsupervised Model and Discriminative Reranking
Li, C., & Liu, Y. (2014) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.3115/v1/p14-3012 · Cited by 36
Combining Translation and Language Model Scoring for Domain-Specific Data Filtering
Mansour, S., Wuebker, J., & Ney, H. (2011) · Cited by 36
Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning
Liu, L., Finch, A., Utiyama, M., & Sumita, E. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10327 · Cited by 35
Candidate re-ranking for SMT-based grammatical error correction
Yuan, Z., Briscoe, T., & Felice, M. (2016) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w16-0530 · Cited by 36
SHEF-Multimodal: Grounding Machine Translation on Images
Shah, K., Wang, J., & Specia, L. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2363 · Cited by 36
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
Wu, J., Wang, X. E., & Wang, W. Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1120 · Cited by 37
“Initialization We use Moses scripts (Koehn et al., 2007) for tokenization.”
Learning to combine Grammatical Error Corrections
Kantor, Y., Katz, Y., Choshen, L., Cohen-Karlik, E., Liberman, N., Toledo, A., Menczel, A., & Slonim, N. (2019) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4414 · Cited by 38
“e cases, we detokenized the input data sets and then tokenized again to perform the combination and evaluation steps. For training the Nematus network, we passed the data tokenization and truecasing (Koehn et al., 2007) and trained BPE (Sennrich et al., 2015). 2.2 Synthetic Error Generation Generating training data fo…”
MethodInfluentialGenerating Diverse Translations with Sentence Codes
Shu, R., Nakayama, H., & Cho, K. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1177 · Cited by 39
“For the ASPEC Ja-En dataset, we use the Moses toolkit (Koehn et al., 2007) to tokenize the English side and Kytea (Neubig et al., 2011) to tokenize the Japanese side.”
MethodVALHALLA: Visual Hallucination for Machine Translation
Li, Y., Panda, R., Kim, Y., Chen, C., Feris, R., Cox, D. D., & Vasconcelos, N. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00515 · Cited by 60
An architecture for Malay Tweet normalization
Saloot, M. A., Idris, N. B., & Mahmud, R. B. (2014) · Information Processing & Management · doi:10.1016/j.ipm.2014.04.009 · Cited by 38
Modeling machine transliteration as a phrase based statistical machine translation problem
Rama, T., & Gali, K. (2009) · Named Entities Workshop (NEWS) · doi:10.3115/1699705.1699737 · Cited by 36
Germann, U., Joanis, E., & Larkin, S. (2009) · doi:10.3115/1621947.1621952 · Cited by 36
Kushalnagar, P., Smith, S. R., Hopper, M., Ryan, C., Rinkevich, M., & Kushalnagar, R. (2016) · Journal of Cancer Education · doi:10.1007/s13187-016-1059-5 · Cited by 36
Revisiting Activation Regularization for Language RNNsPreprint
Merity, S., McCann, B., & Socher, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.01009 · Cited by 36
“Each paragraph is a separate sample of text with the tokens following Moses (Koehn et al., 2007), joining words with @-@ and dot-decimal split to a @.”
Saleh, S., & Pecina, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.613 · Cited by 35
“Moses (with its default settings) is used to train a phrase-table model using the tokenized and lowercased English word forms, and the tokenized and lem-matized data in the target language plus a 5-gram language model.”
MethodExtremely low-resource neural machine translation for Asian languages
Rubino, R., Marie, B., Dabre, R., Fujita, A., Utiyama, M., & Sumita, E. (2020) · Machine Translation · doi:10.1007/s10590-020-09258-6 · Cited by 38
“Instead, to follow a standard configuration typically used for SMT, we tokenized the data using Moses tokenizer for English, Malay, and Vietnamese, only specifying the language option “-l en” for these three languages.”
MethodInfluentialDivide and Translate: Improving Long Distance Reordering in Statistical Machine Translation
Sudoh, K., Duh, K., Tsukada, H., Hirao, T., & Nagata, M. (2010) · Cited by 35
“This sentence is wrong.” Detecting errors in machine-translated sentences
Raybaud, S., Langlois, D., & Smaı̈li, K. (2011) · Machine Translation · doi:10.1007/s10590-011-9094-9 · Cited by 39
Learning Better Monolingual Models with Unannotated Bilingual Text
Burkett, D., Petrov, S., Blitzer, J. C., & Klein, D. (2010) · Cited by 34
Model With Minimal Translation Units, But Decode With Phrases
Durrani, N., Fraser, A., & Schmid, H. (2013) · Cited by 35
Sentence Level Dialect Identification for Machine Translation System Selection
Salloum, W., Elfardy, H., Alamir-Salloum, L., Habash, N. Y., & Diab, M. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2125 · Cited by 35
Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation
Razmara, M., Siahbani, M., Haffari, R., & Sarkar, A. (2013) · Cited by 35
Investigating the Usefulness of Generalized Word Representations in SMT
Durrani, N., Koehn, P., Schmid, H., & Fraser, A. (2014) · International Conference on Computational Linguistics · Cited by 35
Self-citationOptimization for Statistical Machine Translation: A Survey
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Latent Domain Translation Models in Mix-of-Domains Haystack
Hoang, C., & Sima’an, K. (2014) · International Conference on Computational Linguistics · Cited by 35
CSLM — a modular open-source continuous space language modeling toolkit
Schwenk, H. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-326 · Cited by 35
Elnaggar, A., Gebendorfer, C., Glaser, I., & Matthes, F. (2018) · Artificial Intelligence and Cloud Computing Conference (AICCC) · doi:10.1145/3299819.3299844 · Cited by 36
Text Analysis in Adversarial Settings
Gröndahl, T., & Asokan, N. (2019) · ACM Computing Surveys · doi:10.1145/3310331 · Cited by 46
“Iterations Success [14] Google, Bing German, Japanese 1 − 2 No [22] Google, Bing Japanese, German 1 − 2 No [4] Google (Random) ≤ 9 Yes [105] (Not told) Arabic, Chinese, Spanish 1 − 3 Yes [36] Google Spanish, Chinese 1 − 3 Yes [81] Moses German, French 2 Unclear / No [157] Google (DNN) German, French, Spanish, Finnish,…”
MethodFilling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection
Moryossef, A., Aharoni, R., & Goldberg, Y. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3807 · Cited by 43
“We use the multi-bleu.perl script from the Moses toolkit (Koehn et al., 2007).”
Prediction of Learning Curves in Machine Translation
Kolachina, P., Cancedda, N., Dymetman, M., & Venkatapathy, S. (2012) · Cited by 35
Feasibility of human-in-the-loop minimum error rate training
Zaidan, O. F., & Callison-Burch, C. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699510.1699518 · Cited by 35
Self-citationMachine Translation for Subtitling: A Large-Scale Evaluation
Etchegoyhen, T., Bywood, L., Fishel, M., Georgakopoulou, P., Jiang, J., van Loenhout, G., del Pozo, A., Maučec, M. S., Turner, A., & Volk, M. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/32ck2dfzaqpx · Cited by 35
State Of The Art In Translation Memory Technology
Reinke, U. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1291929 · Cited by 35
On the practice of error analysis for machine translation evaluation
Stymne, S., & Ahrenberg, L. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4cs6fjcs27qi · Cited by 36
Sparse Lexicalised Features and Topic Adaptation for SMT
Hasler, E., Haddow, B., & Koehn, P. (2012) · Cited by 34
Self-citationBrivá-Iglesias, V., O’Brien, S., & Cowan, B. R. (2023) · Translation Cognition & Behavior · doi:10.1075/tcb.00077.bri · Cited by 34
Self-citationGerman Compounds in Factored Statistical Machine Translation
Stymne, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85287-2_44 · Cited by 34
Extraction of Multi-word Expressions from Small Parallel Corpora
Tsvetkov, Y., & Wintner, S. (2010) · Cited by 34
Referential Translation Machines for Quality Estimation
Biçici, E. (2013) · Arrow@dit (Dublin Institute of Technology) · Cited by 34
Improving statistical machine translation by paraphrasing the training data.
Bond, F., Nichols, E., Appling, D. S., & Paul, M. D. (2008) · Cited by 34
Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation
Wang, R., Zhao, H., Lu, B., Utiyama, M., & Sumita, E. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2425220 · Cited by 34
Character-based Neural Machine Translation
Costa‐jussà, M. R., & Fonollosa, J. A. R. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2058 · Cited by 260
Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation
Zheng, H., Cheng, Y., & Liu, Y. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/594 · Cited by 34
“All sentences are tokenized by the tokenize.perl script [Koehn et al., 2007].”
MethodFerreira, T. C., Calixto, I., Wubben, S., & Krahmer, E. (2017) · International Conference on Natural Language Generation · doi:10.18653/v1/w17-3501 · Cited by 33
“We used a standard PBMT system built using Moses toolkit (Koehn et al., 2007).”
MethodImproving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation
Li, Z., & Specia, L. (2019) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d19-5543 · Cited by 45
“nce on the validation set are evaluated on both noisy (MTNT and MTNT2019) and clean(newstest2014 andnewsdiscusstest2015) test sets. For prepossessing, we first tokenized the data with Moses tokenizer (Koehn et al., 2007) and applied Byte Pair Encoding (BPE) (Sennrich et al., 2016b) to segment words into subwords. We ex…”
MethodOjugo, A. A., & Eboka, A. O. (2020) · International Journal of Informatics and Communication Technology (IJ-ICT) · doi:10.11591/ijict.v9i1.pp9-18 · Cited by 34
Region-attentive multimodal neural machine translation
Zhao, Y., Komachi, M., Kajiwara, T., & Chu, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.076 · Cited by 38
Normalization of informal text
Pennell, D. L., & Liu, Y. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.07.001 · Cited by 34
A rule-based translation from written Spanish to Spanish Sign Language glosses
Porta, J., Lopez-Colino, F. J., Tejedor, J., & Colás, J. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.10.003 · Cited by 34
Alternative structures for character-level RNNsPreprint
Bojanowski, P., Joulin, A., & Mikolov, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06303 · Cited by 34
Exploration and exploitation of multilingual data for statistical machine translation
Carter, S. (2012) · UvA-DARE (University of Amsterdam) · Cited by 35
A Generalized Framework of Sequence Generation with Application to Undirected Sequence ModelsPreprint
Mansimov, E., Wang, A., Welleck, S., & Cho, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12790 · Cited by 43
“We preprocess this dataset by tokenizing each sentence using a script from Moses (Koehn et al., 2007) and then segmenting each word into subword units using byte pair encoding (Sennrich et al.”
MethodA Generic Framework for Multiword Expressions Treatment: from Acquisition to Applications
Ramisch, C. (2012) · Cited by 33
Dramé, K., Diallo, G., Delva, F., Dartigues, J., Mouillet, É., Salamon, R., & Fleur Mougin, A. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.013 · Cited by 33
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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