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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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Items 201–300 of 1,004
Overview of the 8th Workshop on Asian Translation
Nakazawa, T., Nakayama, H., Ding, C., Dabre, R., Higashiyama, S., Mino, H., Goto, I., Pa, W. P., Kunchukuttan, A., Parida, S., et al. (2021) · Workshop on Asian Translation (WAT) · doi:10.18653/v1/2021.wat-1.1 · Cited by 111
Self-citationQuality Expectations of Machine Translation
Way, A. (2018) · Machine translation · doi:10.1007/978-3-319-91241-7_8 · Cited by 106
What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise
Moorkens, J. (2018) · The Interpreter and Translator Trainer · doi:10.1080/1750399x.2018.1501639 · Cited by 106
“…Google Translate has been increasingly adopting neural methods, Microsoft have released online NMT engines, Kantan MT has created an NMT product called NeuralFleet, the ModernMT project has moved to NMT, 3 despite being led by one of the creators of the popular Moses SMT system (Koehn et al. 2007).”
MethodUnderstanding Back-Translation at ScalePreprint
Edunov, S., Ott, M., Auli, M., & Grangier, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09381 · Cited by 307
A Joint Sequence Translation Model with Integrated Reordering
Durrani, N., Schmid, H., & Fraser, A. (2011) · Cited by 105
Dirt cheap web-scale parallel text from the Common Crawl
Smith, J., Saint-Amand, H., Plamadă, M., Koehn, P., Callison-Burch, C., & Lopez, A. (2013) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-80038 · Cited by 105
Self-citationSix Challenges for Neural Machine Translation
Koehn, P., & Knowles, R. (2017) · Proceedings of the First Workshop on Neural Machine Translation · doi:10.18653/v1/w17-3204 · Cited by 184
“n that were deleted. Nematus9 (Sennrich et al., 2017), with byte-pair encodings (Sennrich et al., 2016b) to allow for open-vocabulary NMT. The phrase-based model that we used was trained using Moses (Koehn et al., 2007), and the training data and parameters match those described in Johns Hopkins University’s submissio…”
MethodInfluentialSelf-citationEnriching Morphologically Poor Languages for Statistical Machine Translation
Avramidis, E., & Koehn, P. (2008) · Cited by 104
Self-citationGrammatical error correction using hybrid systems and type filtering
Felice, M., Yuan, Z., Andersen, Ø. E., Yannakoudakis, H., & Kochmar, E. (2014) · doi:10.3115/v1/w14-1702 · Cited by 103
Effective Domain Mixing for Neural Machine Translation
Britz, D., Le, Q. V., & Pryzant, R. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4712 · Cited by 104
“For the EN-FR and EN-ZH tasks, we follow the WMT ’16 guidlines and tokenize with the Moses tokenizer.perl script (Koehn et al., 2007).”
MethodBarone, A. V. M., & Sennrich, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.02275 · Cited by 123
Arabizi Detection and Conversion to Arabic
Darwish, K. M. (2014) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.3115/v1/w14-3629 · Cited by 102
Addressing the Rare Word Problem in Neural Machine TranslationPreprint
Luong, M.-T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.8206 · Cited by 101
Aiding Pronoun Translation with Co-Reference Resolution
Nagard, R. L., & Koehn, P. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 100
Self-citationIdentifying Causal Relations Using Parallel Wikipedia Articles
Hidey, C., & McKeown, K. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1135 · Cited by 100
Understanding Subtitles by Character-Level Sequence-to-Sequence Learning
Zhang, H., Li, J., Ji, Y., & Yue, H. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2601521 · Cited by 100
“RNNembed can already achieve slightly better performance than word-based RNNsearch and deliver results comparable to Moses.”
MethodInfluentialA neural framework for retrieval and summarization of source code
Chen, Q., & Zhou, M. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3240471 · Cited by 100
“MOSES [16]: A phrase-based machine translation system.”
Language Models for Machine Translation: Original vs. Translated Texts
Lembersky, G., Ordan, N., & Wintner, S. (2012) · Computational Linguistics · doi:10.1162/coli_a_00111 · Cited by 100
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering
Koehn, P., Khayrallah, H., Heafield, K., & Forcada, M. L. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6453 · Cited by 99
“The quality of the resulting subsets was determined by the quality of a statistical machine translation (Moses, phrase-based) and a neural machine translation system (Marian) trained on this data.”
MethodInfluentialSelf-citationMachine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
Vanmassenhove, E., Shterionov, D., & Gwilliam, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.188 · Cited by 99
A Survey of Deep Learning Techniques for Neural Machine TranslationPreprint
Yang, S., Wang, Y., & Chu, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07526 · Cited by 176
“Translation [3] [4], and the Neural Machine Translation [7].”
BackgroundLatent-variable modeling of string transductions with finite-state methods
Dreyer, M., Smith, J., & Eisner, J. M. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613856 · Cited by 98
Two languages are better than one (for syntactic parsing)
Burkett, D., & Klein, D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613828 · Cited by 97
CCG supertags in factored statistical machine translation
Birch, A., Osborne, M., & Koehn, P. (2007) · Conference on Machine Translation (WMT) · doi:10.3115/1626355.1626357 · Cited by 98
Self-citationLearning to rank with partially-labeled data
Duh, K., & Kirchhoff, K. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390379 · Cited by 101
Generation from Abstract Meaning Representation using Tree Transducers
Flanigan, J., Dyer, C., Smith, N. A., & Carbonell, J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1087 · Cited by 97
Self-citationSemantic Role Features for Machine Translation
Liu, D., & Gildea, D. (2010) · Cited by 96
A Gibbs sampler for phrasal synchronous grammar induction
Blunsom, P., Cohn, T., Dyer, C., & Osborne, M. (2009) · doi:10.3115/1690219.1690256 · Cited by 96
Self-citationSampling alignment structure under a Bayesian translation model
DeNero, J., Bouchard‐Côté, A., & Klein, D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613758 · Cited by 97
Personalized Machine Translation: Preserving Original Author Traits
Rabinovich, E., Patel, R. N., Mirkin, S., Specia, L., & Wintner, S. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1101 · Cited by 95
Survey of Automatic Spelling Correction
Hládek, D., Staš, J., & Pleva, M. (2020) · Electronics · doi:10.3390/electronics9101670 · Cited by 94
Al-Thanyyan, S. S., & Azmi, A. M. (2021) · ACM Computing Surveys · doi:10.1145/3442695 · Cited by 114
“Four baselines were compared to TSM: Moses [66], a sentence compression system [47], an enhanced version of a sentence compression system with a lexical component, and a sentence compression system the perform sentence splitting on the output of the previous system.”
BackgroundMethodInfluentialReddy, S., Raghu, D., Khapra, M. M., & Joshi, S. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1036 · Cited by 93
A process study of computer-aided translation
Koehn, P. (2009) · Machine Translation · doi:10.1007/s10590-010-9076-3 · Cited by 93
Self-citationRobust multilingual Named Entity Recognition with shallow semi-supervised features
Agerri, R., & Rigau, G. (2016) · Artificial Intelligence · doi:10.1016/j.artint.2016.05.003 · Cited by 89
Deep Learning Under Privileged Information Using Heteroscedastic Dropout
Lambert, J., Sener, O., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00926 · Cited by 93
Domain adaptation for statistical machine translation with domain dictionary and monolingual corpora
Wu, H. R., Wang, H., & Zong, C. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599206 · Cited by 92
Using a maximum entropy model to build segmentation lattices for MT
Dyer, C. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620754.1620814 · Cited by 92
Self-citationFill-up versus Interpolation Methods for Phrase-based SMT Adaptation
Bisazza, A., Ruiz, N., & Federico, M. (2011) · Cited by 92
Self-citationThe challenging task of summary evaluation: an overview
Lloret, E., Plaza, L., & Aker, A. (2017) · Language Resources and Evaluation · doi:10.1007/s10579-017-9399-2 · Cited by 92
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation
Dalvi, F., Durrani, N., Sajjad, H., & Vogel, S. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2079 · Cited by 92
“1 as opposed to the traditional phrase-based decoder (Moses), which requires pre-computation of phrase-table, future-cost estimation (Durrani et al., 2013a) and separately maintaining each state-full feature (language model, OSM (Durrani et al., 2015) etc.) ⊲ commit all new words if we have seen the entire source the…”
MethodConvergence of Translation Memory and Statistical Machine Translation
Koehn, P., & Sénellart, J. (2010) · Cited by 92
Self-citationTask-based Evaluation of Multiword Expressions: a Pilot Study in Statistical Machine Translation
Carpuat, M. J., & Diab, M. (2010) · Cited by 91
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation
Liang, T., Wong, D. F., Chao, L. S., Quaresma, P., Oliveira, F. C. M. B., Yi, L., Li, S., Wang, Y., & Wang, L. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5g372droqb3q · Cited by 91
The AMARA Corpus: Building Parallel Language Resources for the Educational Domain
Abdelalí, A., Guzmán, F., Sajjad, H., & Vogel, S. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47mc9uc5rj4q · Cited by 90
Saggion, H. (2017) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02166-4 · Cited by 92
Sentence Embedding for Neural Machine Translation Domain Adaptation
Wang, R., Finch, A., Utiyama, M., & Sumita, E. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2089 · Cited by 90
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation
Durrani, N., Sajjad, H., Hoang, H., & Koehn, P. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers · doi:10.3115/v1/e14-4029 · Cited by 89
Self-citationA Neural Attention Model for Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Cited by 89
Post-editing Effort of a Novel With Statistical and Neural Machine Translation
Toral, A., Wieling, M. B., & Way, A. (2018) · Frontiers in Digital Humanities · doi:10.3389/fdigh.2018.00009 · Cited by 89
“The PBMT system is trained on a linear interpolation of in-domain (133 parallel novels from different genres amounting to over 1 million sentence pairs) and out-of-domain (around 400,000 sentence pairs of subtitles)2 parallel data, with version 3 of the Moses toolkit (Koehn et al., 2007).”
MethodBad Characters: Imperceptible NLP Attacks
Boucher, N., Shumailov, I., Anderson, R., & Papernot, N. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833641 · Cited by 102
Semi-supervised Learning for Neural Machine Translation
Cheng, Y. (2019) · Springer theses · doi:10.1007/978-981-32-9748-7_3 · Cited by 91
Semi-Supervised Learning for Neural Machine Translation
Cheng, Y., Xu, W. H., He, Z., He, W. P., Wu, H. R., Sun, M., & Liu, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1185 · Cited by 93
ConvLab: Multi-Domain End-to-End Dialog System Platform
Lee, S., Zhu, Q., Takanobu, R., Zhang, Z., Zhang, Y., Li, X., Li, J., Peng, B., Li, X., Huang, M., et al. (2019) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/p19-3011 · Cited by 103
“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.”
BackgroundUnicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual TasksPreprint
Huang, H., Liang, Y., Duan, N., Gong, M., Shou, L., Jiang, D., & Zhou, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00964 · Cited by 94
“For tokenization, we follows the line of Koehn et al. (2007); Chang et al. (2008) for each language.”
MethodFine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Abhishek, A., Anand, A., & Awekar, A. C. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1075 · Cited by 86
A Comparative Study on End-to-End Speech to Text Translation
Bahar, P., Bieschke, T., & Ney, H. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003774 · Cited by 86
“Moses toolkit [33]3 is used for tokenization.”
Xu, W. H., Ritter, A., Dolan, B., Grishman, R., & Cherry, C. A. (2012) · NPARC · Cited by 85
Detecting sentiment embedded in Arabic social media – A lexicon-based approach
Duwairi, R., Ahmed, N. A., & Al-rifai, S. (2015) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-151574 · Cited by 87
Arabic Diacritization: Stats, Rules, and Hacks
Darwish, K. M., Mubarak, H., & Abdelalí, A. (2017) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.18653/v1/w17-1302 · Cited by 85
Nakov, P., & Tiedemann, J. (2012) · Cited by 84
Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation
Schwenk, H., Rousseau, A., & Attik, M. (2012) · HAL · Cited by 84
Joint Copying and Restricted Generation for Paraphrase
Cao, Z., Luo, C., Li, W., & Li, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10979 · Cited by 84
Guiding Neural Machine Translation Decoding with External Knowledge
Chatterjee, R., Negri, M., Turchi, M., Federico, M., Specia, L., & Blain, F. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4716 · Cited by 84
Self-citationLearning linear ordering problems for better translation
Tromble, R. W., & Eisner, J. M. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699571.1699644 · Cited by 84
Plagiarism Detection across Distant Language Pairs
Barrón‐Cedeño, A., Rosso, P., Agirre, E., & Labaka, G. (2010) · Cited by 83
OpenNMT: Neural Machine Translation ToolkitPreprint
Klein, G., Kim, Y., Deng, Y., Nguyen, V. A., Sénellart, J., & Rush, A. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11462 · Cited by 106
“For comparison the Moses SMT framework including language modeling is over 100K lines.”
BackgroundSemantic Parsing as Machine Translation
Andreas, J., Vlachos, A., & Clark, S. C. (2013) · Cited by 82
Zhang, L., Li, L., & Li, T. (2015) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2783702.2783704 · Cited by 85
Latent Predictor Networks for Code GenerationPreprint
Ling, W., Grefenstette, E., Hermann, K. M., Kočiský, T., Senior, A., Wang, F., & Blunsom, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06744 · Cited by 82
Goodness: A Method for Measuring Machine Translation Confidence
Bách, N., Huang, F., & Al-Onaizan, Y. (2011) · Cited by 81
Non-Parametric Adaptation for Neural Machine Translation
Bapna, A., & Fırat, O. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1191 · Cited by 88
“All datasets are tokenized with the Moses tok-enizer (Koehn et al., 2007) and mixed without any sampling.”
MethodMultimodal machine translation through visuals and speech
Sulubacak, U., Çağlayan, O., Grönroos, S.-A., Rouhe, A., Elliott, D., Specia, L., & Tiedemann, J. (2020) · Machine Translation · doi:10.1007/s10590-020-09250-0 · Cited by 104
“…in machine translation by allowing fast prototyping of new approaches as well as the extension of existing ones to new tasks and paradigms: Moses (Koehn et al, 2007) for SMT, and FairSeq (Ott et al, 2019), Lingvo (Shen et al, 2019), Marian (Junczys-Dowmunt et al, 2018), Nematus (Sennrich et al,…”
MethodInfluentialLatest trends in hybrid machine translation and its applications
Costa‐jussà, M. R., & Fonollosa, J. A. R. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.11.001 · Cited by 81
Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation
Salloum, W., & Habash, N. Y. (2011) · doi:10.7916/d8697bg2 · Cited by 80
Jane: Open Source Hierarchical Translation, Extended with Reordering and Lexicon Models
Vilar, D., Stein, D. J., Huck, M., & Ney, H. (2010) · Cited by 80
Optimizing Transformer for Low-Resource Neural Machine Translation
Araabi, A., & Monz, C. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.304 · Cited by 98
Target-side Word Segmentation Strategies for Neural Machine Translation
Huck, M., Rieß, S., & Fraser, A. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4706 · Cited by 79
A Systematic Exploration of Diversity in Machine Translation
Gimpel, K., Batra, D., Dyer, C., & Shakhnarovich, G. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1111 · Cited by 79
Self-citationA Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines
Burchardt, A., Macketanz, V., Dehdari, J., Heigold, G., Peter, J.-T., & Williams, P. J. (2017) · The Prague Bulletin of Mathematical Linguistics · doi:10.1515/pralin-2017-0017 · Cited by 78
“This includes a 5-gram modified Kneser-Ney language model, mkcls and MGiza for alignment, GDFA phrase extraction with a maximum phrase length of five, msd-bidi-fe lexical reordering, and the Moses decoder (Koehn et al., 2007).”
MethodOptimizing Segmentation Strategies for Simultaneous Speech Translation
Oda, Y., Neubig, G., Sakti, S., Toda, T., & Nakamura, S. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2090 · Cited by 78
462 Machine Translation Systems for Europe
Koehn, P., Birch, A., & Steinberger, R. (2009) · Cited by 78
Self-citationModeling Coherence for Neural Machine Translation with Dynamic and Topic CachesPreprint
Kuang, S., Xiong, D., Luo, W., & Zhou, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11221 · Cited by 78
“From the table, we can find that our implementation RNNSearch* using the feedback attention and dropout outperforms GroundHog and Moses by 1.33 BLEU points and 2.65 BLEU points, respectively.”
InfluentialImproving Lexical Choice in Neural Machine Translation
Nguyen, T. Q., & Chiang, D. (2017) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n18-1031 · Cited by 90
Exploring different representational units in English-to-Turkish statistical machine translation
Oflazer, K., & El-Kahlout, İ. D. (2007) · Conference on Machine Translation (WMT) · doi:10.3115/1626355.1626359 · Cited by 77
Bridging SMT and TM with Translation Recommendation
He, Y., Ma, Y., van Genabith, J., & Way, A. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 77
Grammar Error Correction in Morphologically Rich Languages: The Case of Russian
Rozovskaya, A., & Roth, D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00251 · Cited by 76
Twig: Multi-Agent Task Management for Colocated Latency-Critical Cloud Services
Nishtala, R., Petrucci, V., Carpenter, P., & Själander, M. (2020) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca47549.2020.00023 · Cited by 76
THUMT: An Open Source Toolkit for Neural Machine TranslationPreprint
Zhang, J., Ding, Y., Shen, S., Cheng, Y., Sun, M., Luan, H., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06415 · Cited by 82
Discourse in Statistical Machine Translation
Hardmeier, C. (2012) · Discours · doi:10.4000/discours.8726 · Cited by 118
Measuring Machine Translation Errors in New Domains
Irvine, A., Morgan, J., Carpuat, M. J., Daumé, H., & Munteanu, D. S. (2013) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00239 · Cited by 75
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora
Xu, H., & Koehn, P. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1319 · Cited by 75
“To evaluate the quality, we train a Moses (Koehn et al., 2007) SMT system on selected data, and evaluate each trained SMT system on 3 test corpora: newstest2011 which contains 3003 sentence pairs, and a random subset of the TED-talks corpus and the movie-subtitle corpus from OPUS (Tiedemann, 2012),…”
MethodSelf-citationStory Generation from Sequence of Independent Short DescriptionsPreprint
Jain, P., Agrawal, P., Mishra, A., Sukhwani, M., Laha, A., & Sankaranarayanan, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05501 · Cited by 75
“For SMT related experiments, we use the MOSES toolkit [13].”
MethodJunczys-Dowmunt, M., & Grundkiewicz, R. (2014) · doi:10.3115/v1/w14-1703 · Cited by 74
Does Multimodality Help Human and Machine for Translation and Image Captioning?
Çağlayan, O., Aransa, W., Wang, Y., Masana, M., García-Martínez, M., Bougares, F., Barrault, L., & van de Weijer, J. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2358 · Cited by 74
Grammatical Error Correction: Machine Translation and Classifiers
Rozovskaya, A., & Roth, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1208 · Cited by 74
On the Linguistic Representational Power of Neural Machine Translation Models
Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., & Glass, J. R. (2020) · Computational Linguistics · doi:10.1162/coli_a_00367 · Cited by 77
The complexity of phrase alignment problems
DeNero, J., & Klein, D. (2008) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1557690.1557698 · Cited by 75
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning
Etoori, P., Chinnakotla, M. K., & Mamidi, R. (2018) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p18-3021 · Cited by 80
Measuring User Productivity in Machine Translation Enhanced Computer Assisted Translation
Federico, M., Cattelan, A. M., & Trombetti, M. (2012) · Conference of the Association for Machine Translation in the Americas · Cited by 74
Self-citation
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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