Fetching the paper…
Reading the bibliography…
Data selection techniques applied to neural machine translation (NMT) aim to increase the performance of a model by retrieving a subset of sentences for use as training data.
V. Levenshtein, “Binary codes capable of correcting deletions, insertions and reversals,” in Soviet Physics Doklady , vol. 10, no. 8, 1966, pp. 707–710
1966
Earlier work this paper cites.
V. N. Vapnik, Statistical Learning Theory . Wiley-Interscience, 1998
1998
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of 40th Annual Meeting of the Association for Computational Linguistics , Philadelphia, Pennsylvania, USA, July 2002, pp. 311–318
2002
Earlier work this paper cites.
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for MT evaluation with improved correlation with human judgments,” in Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , Ann Arbor, Michigan, 2005, pp. 65–72
2005
Earlier work this paper cites.
M. Snover, B. Dorr, R. Schwartz, L. Micciulla, and J. Makhoul, “A study of translation edit rate with targeted human annotation,” in Proceedings of the 7th Conference of the Association for Machine Translation in the Americas , Cambridge, Massachusetts, USA, 2006, pp. 223–231
2006
Earlier work this paper cites.
R. C. Moore and W. Lewis, “Intelligent selection of language model training data,” in Proceedings of the ACL 2010 conference short papers , Uppsala, Sweden, 2010, pp. 220–224
2010
Earlier work this paper cites.
E. Biçici and D. Yuret, “Instance selection for machine translation using feature decay algorithms,” in Proceedings of the Sixth Workshop on Statistical Machine Translation , Edinburgh, Scotland, 2011, pp. 272–283
2011
Earlier work this paper cites.
A. Axelrod, X. He, and J. Gao, “Domain adaptation via pseudo in-domain data selection,” in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing , Edinburgh, Scotland, UK., 2011, pp. 355–362
2011
Cited alongside, same era.
J. H. Clark, C. Dyer, A. Lavie, and N. A. Smith, “Better hypothesis testing for statistical machine translation: Controlling for optimizer instability,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) , Portland, Oregon, 2011, p. 176–181
2011
Cited alongside, same era.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems , 2013, pp. 3111–3119
2013
Cited alongside, same era.
E. Biçici, “Feature decay algorithms for fast deployment of accurate statistical machine translation systems,” in Proceedings of the Eighth Workshop on Statistical Machine Translation , Sofia, Bulgaria, August 2013, pp. 78–84
M. Popovic, “chrF: character n-gram F-score for automatic MT evaluation,” in Proceedings of the Tenth Workshop on Statistical Machine Translation , Lisbon, Portugal, 2015, pp. 392–395
2015
Later among the works it cites.
R. Sennrich, B. Haddow, and A. Birch, “Improving neural machine translation models with monolingual data,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Berlin, Germany, 2016, pp. 86–96
2016
Later among the works it cites.
A. Poncelas, A. Way, and A. Toral, “Extending feature decay algorithms using alignment entropy,” in International Workshop on Future and Emerging Trends in Language Technology , Seville, Spain, 2016, pp. 170–182
2016
Later among the works it cites.
M. van der Wees, A. Bisazza, and C. Monz, “Dynamic data selection for neural machine translation,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , Copenhagen, Denmark, 2017, pp. 1400–1410
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
E. Biçici, Q. Liu, and A. Way, “ParFDA for fast deployment of accurate statistical machine translation systems, benchmarks, and statistics,” in Proceedings of the Tenth Workshop on Statistical Machine Translation , Lisbon, Portugal, 2015, pp. 74–78
2015
Cited alongside, same era.
E. Biçici and D. Yuret, “Optimizing instance selection for statistical machine translation with feature decay algorithms,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 23, no. 2, pp. 339–350, 2015
2015
Cited alongside, same era.
S. Eetemadi, W. Lewis, K. Toutanova, and H. Radha, “Survey of data-selection methods in statistical machine translation,” Machine Translation , vol. 29, no. 3-4, pp. 189–223, 2015
2015
Cited alongside, same era.
O. Bojar, R. Chatterjee, C. Federmann, B. Haddow, M. Huck, C. Hokamp, P. Koehn, V. Logacheva, C. Monz, M. Negri, M. Post, C. Scarton, L. Specia, and M. Turchi, “Findings of the 2015 Workshop on Statistical Machine Translation,” in Proceedings of the Tenth Workshop on Statistical Machine Translation , Lisboa, Portugal, September 2015, pp. 1–46
2015
Cited alongside, same era.
Later among the works it cites.
A. Poncelas, G. M. de Buy Wenniger, and A. Way, “Applying n-gram alignment entropy to improve feature decay algorithms,” The Prague Bulletin of Mathematical Linguistics , vol. 108, no. 1, pp. 245–256, 2017
2017
Later among the works it cites.
G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush, “Opennmt: Open-source toolkit for neural machine translation,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations , Vancouver, Canada, 2017, pp. 67–72
2017
Later among the works it cites.
A. Poncelas, G. M. de Buy Wenniger, and A. Way, “Feature decay algorithms for neural machine translation,” in Proceedings of the 21st Annual Conference of the European Association for Machine Translation , Alacant, Spain, 2018, pp. 239–248
2018
Closest in time.
X. Li, J. Zhang, and C. Zong, “One Sentence One Model for Neural Machine Translation,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , Miyazaki, Japan, 2018, pp. 910–917
2018
Closest in time.