[1] TILLMANN C. A unigram orientation model for statistical machine translation[C]//Proceedings of the 2004 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2004:101-104. [2] GILDEA D, KHUDANPUR S, SARKAR A, et al. A smorgasbord of features for statistical machine translation[C]//Proceedings of the 2004 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2004:161-168. [3] LI P, LIU Y, SUN M, et al. A neural reordering model for phrase-based translation[C]//Proceedings of the 2014 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2014:1897-1907. [4] 董兴华, 陈丽娟, 周喜, 等. 汉维统计机器翻译中的形态学处理[J]. 计算机工程, 2011, 37(12):150-152.(DONG X H, CHEN L J, ZHOU X, et al. Morphology processing in Chinese-Uyghur statistical machine translation[J]. Computer Engineering, 2011, 37(12):150-152.) [5] 陈丽娟, 张恒, 董兴华, 等. 基于句法调序的汉维统计机器翻译[J]. 计算机工程, 2012, 38(3):169-171.(CHEN L J, ZHANG H, DONG X H, et al. Chinese-Uyghur statistical machine translation based on syntactical reordering[J]. Computer Engineering, 2012, 38(3):169-171.) [6] 孔金英, 李晓, 王磊, 等. 调序规则表的深度过滤研究[J]. 计算机科学与探索, 2017, 11(5):785-793.(KONG J Y, LI X, WANG L, et al. Research of deep filtering lexical reordering table[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(5):785-793.) [7] 杨南, 李沐. 基于神经网络的统计机器翻译的预调序模型[J]. 中文信息学报, 2016, 30(3):103-110.(YANG N, LI M. A neural pre-reordering model for statistical machine translation[J]. Journal of Chinese Information Processing, 2016, 30(3):103-110.) [8] LI P, LIU Y, SUN M. Recursive autoencoders for ITG-based translation[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. New York:ACM, 2013:567-577. [9] GREEN S, GALLEY M, MANNING C D. Improved models of distortion cost for statistical machine translation[C]//Proceedings of the 2010 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2010:867-875. [10] NGUYEN V V, NGUYEN T P, NGUYEN M L, et al. A model lexicalized hierarchical reordering for phrase based translation[J]. Procedia-Social and Behavioral Sciences, 2011, 27:77-85. [11] HADIWINOTO C, NG H T. A dependency-based neural reordering model for statistical machine translation[C]//Proceedings of the 2017 Conference on Artificial Intelligence. Menlo Park, CA:AAAI Press, 2017:109-115. [12] SIVIC J, ZISSERMAN A. Efficient visual search of videos cast as text retrieval[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(4):591-606. [13] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL].[2017-05-10]. https://arxiv.org/abs/1301.3781. [14] MIKOLOV T, YIH W, ZWEIG G. Linguistic regularities in continuous space word representations[C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2013:746-751. [15] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[EB/OL].[2017-05-10]. https://arxiv.org/abs/1310.4546. [16] GREFENSTETTE E, DINU G, ZHANG Y Z, et al. Multi-step regression learning for compositional distributional semantics[EB/OL].[2017-05-10]. http://www.anthology.aclweb.org/W/W13/W13-0112.pdf. [17] LE Q V, MIKOLOV T. Distributed representations of sentences and documents[EB/OL].[2017-05-10]. http://proceedings.mlr.press/v32/le14.pdf. [18] GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]//Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2013:6645-6649. [19] GRAVES A. Generating sequences with recurrent neural networks[EB/OL].[2017-05-10]. https://arxiv.org/abs/1308.0850. [20] FINKEL J R, GRENAGER T, MANNING C. Incorporating non-local information into information extraction systems by Gibbs sampling[C]//Proceedings of the 2005 Conference of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2005:363-370. [21] KOEHN P, HOANG H, BIRCH A, et al. Moses:open source toolkit for statistical machine translation[C]//Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Stroudsburg, PA:Association for Computational Linguistics, 2007:177-180. [22] OCH F J. GIZA++:Training of statistical translation models[EB/OL].[2017-05-10]. https://www.prhlt.upv.es/~evidal/students/doct/sht/transp/giza2p.pdf. [23] STOLCKE A. SRILM-an extensible language modeling toolkit[EB/OL].[2017-05-10]. http://isca-speech.org/archive/archive_papers/icslp_2002/i02_0901.pdf. [24] PAPINENI K, ROUKOS S, WARD T, et al. BLEU:a method for automatic evaluation of machine translation[C]//Proceedings of the 2002 Conference of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2002:311-318. [25] HUANG X, ALLEVA F, HON H, et al. The SPHINX-Ⅱ speech recognition system:an overview[J]. Computer Speech & Language, 1993, 7(2):137-148. [26] MORIN F, BENGIO Y. Hierarchical probabilistic neural network language model[EB/OL].[2017-05-10]. http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf. [27] YANMIN S, ANDREW K C W, MOHAMED S K. Classification of imbalanced data:a review[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2009, 23(4):687-719. [28] LIANG G, ZHANG C. An efficient and simple under-sampling technique for imbalanced time series classification[C]//CIKM 2012:Proceedings of the 21st ACM International Conference on Information and Knowledge Management. New York:ACM, 2012:2339-2342. [29] YU D J, HU J, TANG Z M, et al. Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling[J]. Neurocomputing, 2013, 104:180-190. |