1Schwenk H. Continuous Space Language Models. Computer Speech and Language, 2007, 21 (3) : 492-518.
2Bengio Y, Ducharme R, Vincent P, et al. A Neural Probabilistie Language Model. Journal of Machine Learning Research, 2003, 3 : 1137-1155.
3Mikolov T, Karafiett M, Burger L, et al. Recurrent Neural Network Based Language Model//Proc of the 11 th Annual Conference of the International Speech Communication Association. Makuhari, Japan, 2010:1045-1048.
4Mikolov T, Kombrink S, Burget L, et al. Extensions of Recurrent Neural Network Language Model// Proc of the IEEE International Conference on Acoustics , Speech and Signal Processing . Prague ,Czech Republic, 2011 : 5528-5531.
5Bengio Y, Simard P, Frasconi P. Learning Long-Term Dependen- cies with Gradient Descent Is Difficult. IEEE Trans on Neural Net- works, 1994, 5(2): 157-166.
6Son L H, Allauzen A, Yvon F. Measuring the Influence of Long Range Dependencies with Neural Network Language Models//Prec of the NAACL-HLT Workshop : Will We Ever Really Replace the N- gram Model.'? On the Future of Language Modeling for HLT. Man- treal, Canada, 2012:1-10.
8Sundermeyer M, Schltlter R, Ney H. LSTM Neural Networks for Lan- guage Modeling[EB/OL]. [2014-02-10]. http://www-i6, informatik. rwth- aachen, de/publications/download/820/Sundermeycr - 2012. pdf.
9Shi Y, Wiggers P, Jonker C M. Towards Recurrent Neural Networks Language Models with Linguistic and Contextual Features//Proe of the 13th Annual Conference of the International Speech Communica- tion Association. Portland, USA, 2012:1664-1667.
10Auli M, Galley M, Quirk C, et al. Joint Language and Translation Modeling with Recurrent Neural Networks // Proc of the Confe- rence on Empirical Methods in Natural Language Processing. Sea- ttle, USA, 2013:1044-1054.