The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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.
Cited by
Sorted by citations
Items 501–600 of 1,004
Cross-Lingual Syntactic Transfer with Limited Resources
Rasooli, M. S., & Collins, M. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00061 · Cited by 47
Arabic Machine Translation: A survey of the latest trends and challenges
Ameur, M. S. H., Meziane, F., & Guessoum, A. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100305 · Cited by 62
Saggion, H. (2017) · Synthesis lectures on human language technologies · doi:10.2200/s00700ed1v01y201602hlt032 · Cited by 48
STUDY AND COMPARISON OF RULE-BASED AND STATISTICAL CATALAN-SPANISH MACHINE TRANSLATION SYSTEMS
Costa‐jussà, M. R., Farrús, M., Acebal, J. B. M., & Fonollosa, J. A. R. (2012) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 46
Does more data always yield better translations
Gascó, G., Rocha, M.-A., Sanchis-Trilles, G., Andrés-Ferrer, J., & Casacuberta, F. (2012) · Cited by 46
The latent words language model
Deschacht, K., Belder, J. D., & Moens, M. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.04.001 · Cited by 46
Carpuat, M. J., Marton, Y., & Habash, N. Y. (2011) · Machine Translation · doi:10.1007/s10590-011-9112-y · Cited by 46
Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation
Hardmeier, C., Stymne, S., Tiedemann, J., & Nivre, J. (2013) · Cited by 46
Language-independent compound splitting with morphological operations
Macherey, K., Dai, A. M., Talbot, D., Popat, A. C., & Och, F. J. (2011) · Cited by 46
News Across Languages - Cross-Lingual Document Similarity and Event Tracking
Rupnik, J., Muhič, A., Leban, G., Škraba, P., Fortuna, B., & Grobelnik, M. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4780 · Cited by 47
Generating artificial errors for grammatical error correction
Felice, M., & Yuan, Z. (2014) · Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (EACL SRW) · doi:10.3115/v1/e14-3013 · Cited by 46
DCU-UvA Multimodal MT System Report
Calixto, I., Elliott, D., & Frank, S. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2359 · Cited by 46
Can Semantic Role Labeling Improve SMT
Wu, D., & Fung, P. (2009) · Cited by 46
A Knowledge Management Architecture for Digital Cultural Heritage
Dragoni, M., Tonelli, S., & Moretti, G. (2017) · Journal on Computing and Cultural Heritage · doi:10.1145/3012289 · Cited by 46
“Example of services available through APIs are Google Translate(4) and Microsoft Bing,(5) whereas an example of an open source toolkit for machine translation is Moses (Koehn et al. 2007).”
MethodCoppers, S., den Bergh, J. V., Luyten, K., Coninx, K., van der Lek-Ciudin, I., Vanallemeersch, T., & Vandeghinste, V. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174098 · Cited by 49
“Based on a large set of sentences and their translation (a parallel corpus), Moses automatically extracts word groups and their potential translations, and builds a language model in order to be able to create fluent translations.”
MethodInfluentialUnsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
Huang, P.-Y., Hu, J., Chang, X., & Hauptmann, A. G. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.731 · Cited by 45
Improving Wordnets for Under-Resourced Languages Using Machine Translation
Chakravarthi, B. R., Arčan, M., & McCrae, J. P. (2018) · Global WordNet Conference (GWC) · doi:10.18653/v1/2018.gwc-1.10 · Cited by 45
“To obtain the translations, we use Moses SMT toolkit with of baseline setup with 5-gram language model created using the training data by KenLM (Heafield, 2011).”
MethodInfluentialMager, M., Oncevay, A., Ebrahimi, A., Ortega, J. E., Rios, A., Fan, A., Gutierrez-Vasques, X., Chiruzzo, L., Giménez-Lugo, G. A., Ramos, R., et al. (2021) · Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP) · doi:10.18653/v1/2021.americasnlp-1.23 · Cited by 53
“All data was pre-processed with standard Moses tools (Koehn et al., 2007).”
MethodCASMACAT: A Computer-assisted Translation Workbench
Alabau, V., Buck, C., Carl, M., Casacuberta, F., García-Martínez, M., Germann, U., González-Rubio, J., Hill, R. L., Koehn, P., Leiva, L. A., et al. (2014) · doi:10.3115/v1/e14-2007 · Cited by 45
Self-citationFritzinger, F., & Fraser, A. (2010) · Cited by 45
Bagging and Boosting statistical machine translation systems
Xiao, T., Zhu, J., & Liu, T. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.11.005 · Cited by 46
Building a German/Simple German Parallel Corpus for Automatic Text Simplification
Klaper, D., Ebling, S., & Volk, M. (2013) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-78610 · Cited by 45
Edinburgh’s Phrase-based Machine Translation Systems for WMT-14
Durrani, N., Haddow, B., Koehn, P., & Heafield, K. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3309 · Cited by 45
Self-citationA Neural Network based Approach to Automatic Post-Editing
Pal, S., Naskar, S. K., Vela, M., & van Genabith, J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2046 · Cited by 45
Lexical-Constraint-Aware Neural Machine Translation via Data Augmentation
Chen, G., Chen, Y., Wang, Y., & Li, V. O. K. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/496 · Cited by 49
Encoding Source Language with Convolutional Neural Network for Machine TranslationPreprint
Meng, F., Lu, Z., Wang, M., Li, H., Jiang, W., & Liu, Q. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01838 · Cited by 45
Exact Decoding of Phrase-based Translation Models through Lagrangian Relaxation
Chang, Y.-W., & Collins, M. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 45
Zhang, K. W., & Bowman, S. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10040 · Cited by 45
“We use word-level representations throughout and use the Moses package (Koehn et al., 2007) to tokenize and truecase our data.”
MethodImproved Reordering for Phrase-Based Translation using Sparse Features
Cherry, C. A. (2013) · NPARC · Cited by 44
Learning stochastic bracketing inversion transduction grammars with a cubic time biparsing algorithm
Saers, M., Nivre, J., & Wu, D. (2009) · International Workshop/Conference on Parsing Technologies · doi:10.3115/1697236.1697242 · Cited by 44
Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information
Su, J., Wu, H. R., Wang, H., Chen, Y., Shi, X., Dong, H., & Liu, Q. (2012) · Cited by 44
Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs
Wołk, K., & Marasek, K. P. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.11.024 · Cited by 44
Regularization and search for minimum error rate training
Cer, D. M., Jurafsky, D. S., & Manning, C. D. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626398 · Cited by 44
The Joy of Parallelism with CzEng 1.0
Bojar, O., Žabokrtský, Z., Dušek, O., Galuščáková, P., Majliš, M., Mareček, D., Maršík, J., Novák, M., Popel, M., & Tamchyna, A. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/46mpy5z6qo7y · Cited by 44
Self-citationConvolutional Neural Network Language Models
Pham, N.-Q., Kruszewski, G., & Boleda, G. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1123 · Cited by 44
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Dahlmann, L., Matusov, E., Petrushkov, P., & Khadivi, S. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1148 · Cited by 44
“For the phrase-based baselines, we use an in-house phrase-decoder (Matusov and K¨opr¨u, 2010) which is similar to the Moses decoder (Koehn et al., 2007).”
MethodProcessing Spontaneous Orthography
Eskander, R., Habash, N. Y., Rambow, O., & Tomeh, N. (2013) · North American Chapter of the Association for Computational Linguistics · Cited by 44
A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich LanguagesPreprint
Luong, M.-T., Nakov, P., & Kan, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08117 · Cited by 44
Dynamic Multi-Level Multi-Task Learning for Sentence SimplificationPreprint
Guo, H., Pasunuru, R., & Bansal, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07304 · Cited by 54
“…2014; Glavaˇs and ˇStajner, 2015), as well as treating simplification as a monolingual MT task, where operations are learned from examples of complex-simple sentence pairs (Specia, 2010; Koehn et al., 2007; Coster and Kauchak, 2011; Zhu et al., 2010; Wubben et al., 2012; Narayan and Gardent, 2014).”
BackgroundA Dataset for Movie DescriptionPreprint
Rohrbach, A., Rohrbach, M., Tandon, N., & Schiele, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.02530 · Cited by 57
Ncode: an Open Source Bilingual N-gram SMT Toolkit
Crego, J., Yvon, F., & Mariño, J. (2011) · The Prague Bulletin of Mathematical Linguistics · doi:10.2478/v10108-011-0010-5 · Cited by 43
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Sénellart, J., Zhang, D., Wang, B., Klein, G., Ramatchandirin, J.-P., Crego, J., & Rush, A. M. (2018) · doi:10.18653/v1/w18-2715 · Cited by 44
“After decoding, we remove the BPE joiners and evaluate the tokenized output with multi-bleu.perl (Koehn et al., 2007).”
MethodSampling-based multilingual alignment
Lardilleux, A., & Lepage, Y. (2009) · HAL · Cited by 43
Arabic Machine Transliteration using an Attention-based Encoder-decoder Model
Ameur, M. S. H., Meziane, F., & Guessoum, A. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.10.120 · Cited by 44
End-to-End Speech Translation With Transcoding by Multi-Task Learning for Distant Language Pairs
Kano, T., Sakti, S., & Nakamura, S. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2986886 · Cited by 47
“However, their work was only based on Spanish-English language pairs with similar syntax and word order, and the results failed to outperform the traditional cascade approach based only on a statistical word level MT (MOSES) [9].”
Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A.-E., Dashtipour, K. K., Gogate, M., Ieracitano, C., Kashani, R., & Hussain, A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00510-1 · Cited by 45
“Inspired by [26], we used the open-source Moses toolkit [84] to build a phrase-based MT system with default settings: bidirectional phrase and lexical translation probabilities, distortion model, a word and a phrase penalty and a trigram language model.”
MethodCode-Switching for Enhancing NMT with Pre-Specified TranslationPreprint
Song, K., Zhang, Y., Yu, H., Luo, W., Wang, K., & Zhang, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09107 · Cited by 43
On Using SpecAugment for End-to-End Speech TranslationPreprint
Bahar, P., Zeyer, A., Schlüter, R., & Ney, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08876 · Cited by 48
(2016) · doi:10.18653/v1/n16-1046 · Cited by 94
Online adaptation strategies for statistical machine translation in post-editing scenarios
Martínez-Gómez, P., Sanchis-Trilles, G., & Casacuberta, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.011 · Cited by 42
Paraphrasing 4 Microblog Normalization
Wang, L., Dyer, C., Black, A. W., & Trancoso, I. M. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1008 · Cited by 42
Self-citationMax-Violation Perceptron and Forced Decoding for Scalable MT Training
Yu, H., Huang, L., Mi, H., & Zhao, K. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1112 · Cited by 42
(2012) · Cited by 43
Graphical models over multiple strings
Dreyer, M., & Eisner, J. M. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699510.1699524 · Cited by 42
Quadratic-time dependency parsing for machine translation
Galley, M., & Manning, C. D. (2009) · doi:10.3115/1690219.1690255 · Cited by 42
Consistent Translation using Discriminative Learning - A Translation Memory-inspired Approach
Ma, Y., He, Y., Way, A., & van Genabith, J. (2011) · Cited by 44
Improvements in Dynamic Programming Beam Search for Phrase-based Statistical Machine Translation
Zens, R., & Ney, H. (2008) · Cited by 42
Self-citationMorphological Processing for English-Tamil Statistical Machine Translation
Ramasamy, L., Bojar, O., & ⁄, Z. Å. ⁄. A. (2012) · Cited by 42
Self-citationA quantitative analysis of reordering phenomena
Birch, A., Blunsom, P., & Osborne, M. (2009) · Conference on Machine Translation (WMT) · doi:10.3115/1626431.1626471 · Cited by 42
Self-citationNonparametric Word Segmentation for Machine Translation
Nguyen, T., Vogel, S., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473582 · Cited by 42
Improved Arabic Dialect Classification with Social Media Data
Huang, F. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1254 · Cited by 42
Neishi, M., Sakuma, J., Tohda, S., Ishiwatari, S., Yoshinaga, N., & Toyoda, M. (2017) · Cited by 41
Morphological Pre-Processing for Turkish to English Statistical Machine Translation
Bisazza, A., & Federico, M. (2009) · IWSLT · Cited by 42
Self-citationAdapting Translation Models to Translationese Improves SMT
Lembersky, G., Ordan, N., & Wintner, S. (2012) · Cited by 42
Encoder-Decoder Methods for Text Normalization
Lusetti, M., Ruzsics, T., Göhring, A., Samardżić, T., & Stark, E. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-156775 · Cited by 42
“With a small modification of the input, so that the models are estimated over characters rather than over words, well-known off-the-shelf SMT tools like Moses (Koehn et al., 2007) could be used to obtain significant improvements in comparison to previous solutions.”
MethodInfluentialVector Space Model for Adaptation in Statistical Machine Translation
Chen, B., Kühn, R., & Foster, G. F. (2013) · NPARC · Cited by 41
Continuous space models for CLIR
Gupta, P., Banchs, R. E., & Rosso, P. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.11.002 · Cited by 43
“We train a phrase-based machine translation system on the training parallel data using the standard state-of-the-art Moses toolkit(1) with default parameters (Koehn et al., 2007).”
MethodTacTok: semantics-aware proof synthesis
First, E., Brun, Y., & Guha, A. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428299 · Cited by 59
“When applied to natural languages, these models have aided speech recognition, machine translation, spelling correction, and other natural language processing tasks [Brill and Moore 2000; Koehn et al. 2007; Stolcke 2002].”
MethodA Systematic Comparison of Phrase Table Pruning Techniques
Zens, R., Stanton, D., & Xu, P. (2012) · Cited by 41
Self-citationWord lattices for multi-source translation
Schroeder, J., Cohn, T., & Koehn, P. (2009) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1609067.1609147 · Cited by 41
Self-citationMeaningful variable names for decompiled code
Jaffe, A., Lacomis, J., Schwartz, E. J., Goues, C. L., & Vasilescu, B. (2018) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3196321.3196330 · Cited by 41
Attention With Sparsity Regularization for Neural Machine Translation and Summarization
Zhang, J., Zhao, Y., Li, H., & Zong, C. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2883740 · Cited by 44
“Inspired by statistical machine translation [12]”
Automating App Review Response Generation
Gao, C., Zeng, J., Xia, X., Lo, D., Lyu, M. R.-T., & King, I. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00025 · Cited by 39
“Ritter et al. [73] and Vinyals and Le [74] are the first to treat generation of conversational dialog as a data-driven statistical machine translation (SMT) [75] problem.”
BackgroundBetter Document-Level Machine Translation with Bayes’ Rule
Yu, L., Sartran, L., Stokowiec, W., Wang, L., Kong, L., Blunsom, P., & Dyer, C. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00319 · Cited by 42
“…optionally use document context on the source (conditioning) side, but sentences are generated independently. pected length of the output), and can be incorporated at no cost in the model; they are widely used in prior work (Koehn et al., 2007; Yu et al., 2017; Yee et al., 2019; Ng et al., 2019).”
BackgroundMethodSelf-citationGrapheme to phoneme conversion using an SMT system
Laurent, A., Deléglise, P., & Meignier, S. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-243 · Cited by 41
Weighted alignment matrices for statistical machine translation
Liu, Y., Xia, T., Xiao, X., & Liu, Q. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699571.1699645 · Cited by 41
Adaptation of machine translation for multilingual information retrieval in the medical domain
Pecina, P., Dušek, O., Goeuriot, L., Hajič, J., Hlaváčová, J., Jones, G. J. F., Kelly, L., Leveling, J., Mareček, D., Novák, M., et al. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.01.004 · Cited by 42
Translation as weighted deduction
Lopez, A. (2009) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1609067.1609126 · Cited by 41
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine TranslationPreprint
Chu, C., Dabre, R., & Kurohashi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03214 · Cited by 41
“We used the Moses PBSMT system (Koehn et al., 2007) for all of our MT experiments.”
Local Monotonic Attention Mechanism for End-to-End Speech and Language ProcessingPreprint
Tjandra, A., Sakti, S., & Nakamura, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08091 · Cited by 41
MATREX: The DCU MT System for WMT 2010
Penkale, S., Haque, R., Dandapat, S., Banerjee, P., Srivastava, A. K., Du, J., Pecina, P., Naskar, S. K., Forcada, M. L., & Way, A. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 41
Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili
Pauw, G. D., Wagacha, P. W., & de Schryver, G.-M. (2011) · Language Resources and Evaluation · doi:10.1007/s10579-011-9159-7 · Cited by 40
Zhang, R., Westerfield, C., Shim, S., Bingham, G., Fabbri, A. R., Hu, W., Verma, N., & Radev, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1306 · Cited by 44
“For SMT, we use the moses system (Koehn et al., 2007) with word alignments using mGiza and 5-gram KenLM language model (Heafield, 2011).”
Feldman, I., & Coto‐Solano, R. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.351 · Cited by 40
“Each model was then evaluated against the testing data with the Bilingual Evaluation Understudy Score (BLEU) technique (Papineni et al., 2002) where the specific implementation was the multi-bleu script from Moses (Koehn et al., 2007).”
BackgroundMethodCombining multi-domain statistical machine translation models using automatic classifiers
Banerjee, P., Du, J., Li, B., Naskar, S. K., Way, A., & van Genabith, J. (2010) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 40
Automatic generation of parallel treebanks
Zhechev, V., & Way, A. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599220 · Cited by 40
Improving interactive machine translation via mouse actions
Sanchis-Trilles, G., Ortiz-Martínez, D., Civera, J., Casacuberta, F., Vidal, E., & Hoang, H. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613776 · Cited by 40
Self-citationDialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic
Salloum, W., & Habash, N. Y. (2013) · Cited by 40
Twitter Translation using Translation-Based Cross-Lingual Retrieval
Jehl, L., Hieber, F., & Riezler, S. (2012) · Cited by 40
Triplet lexicon models for statistical machine translation
Hasan, S., Ganitkevitch, J., Ney, H., & Andrés-Ferrer, J. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613764 · Cited by 40
Akhtar, M. S., Sawant, P., Sen, S., Ekbal, A., & Bhattacharyya, P. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1053 · Cited by 40
“The alignment information are obtained from the mosesdecoder (Koehn et al., 2007) during translation of the reviews.”
MethodInfluentialOn the Relation between Position Information and Sentence Length in Neural Machine Translation
Neishi, M., & Yoshinaga, N. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1031 · Cited by 44
“…and Knowles (2017) report that even RNN-based model with the attention mechanism performs worse than phrase-based statistical machine translation (Koehn et al., 2007) in translating very long sentences, which challenges us to develop an NMT model that is robust to long sentences or more…”
BackgroundInfluentialFirst Result on Arabic Neural Machine TranslationPreprint
Almahairi, A., Cho, K., Habash, N. Y., & Courville, A. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02680 · Cited by 58
DEPFIX: A System for Automatic Correction of Czech MT Outputs
Rosa, R., Mareček, D., & Dušek, O. (2012) · Institute of Formal and Applied Linguistics (ÚFAL) · Cited by 39
Optimizing Instance Selection for Statistical Machine Translation with Feature Decay Algorithms
Biçici, E., & Yüret, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2381882 · Cited by 39
Gathering and Generating Paraphrases from Twitter with Application to Normalization
Xu, W. H., Ritter, A., & Grishman, R. (2013) · Cited by 39
Semantic interpretation of noun compounds using verbal and other paraphrases
Nakov, P., & Hearst, M. A. (2013) · ACM Transactions on Speech and Language Processing · doi:10.1145/2483969.2483975 · Cited by 39
A Beam-Search Decoder for Normalization of Social Media Text with Application to Machine Translation
Wang, P., & Ng, H. T. (2013) · Cited by 39
Yeniterzi, R., & Oflazer, K. (2018) · Figshare · doi:10.1184/r1/6368090 · Cited by 39
Graph-based learning for statistical machine translation
Alexandrescu, A., & Kirchhoff, K. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620754.1620772 · Cited by 39
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 ›
Are you an author, or do you see a problem with this page? Request a review
