End-to-End Spanish-English Sequence Learning Translation Model
The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 369
 T. Strauß, “Decoding the output of neural networks - a discriminative approach,” Ph.D. dissertation, University of Rostock, 2017.
 Ron J. Weiss, Jan Chorowski, Navdeep Jaitly, Yonghui Wu, Zhifeng Chen “Sequence-to-Sequence Models Can Directly Translate Foreign Speech” arXiv:1703.08581v2 (cs.CL) 12 Jun 2017
 J. Poulos and R. Valle, “Attention networks for image-to-text,” CoRR, vol. abs/1712.04046, 2017
 Y. Zhang, W. Chan, and N. Jaitly, “Very deep convolutional networks for end-to-end speech recognition,” in Proceedings of ICASSP, 2017.
 D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” ICLR, 12 2014.
 I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems, 2014, pp. 3104–3112
 A. Sriram, H. Jun, S. Satheesh, and A. Coates, “Cold fusion: Training seq2seq models together with language models,” CoRR, vol. abs/1708.06426, 2017
 Ofir Press and Lior Wolf “Using the Output Embedding to Improve Language Models” arXiv:1608.05859v3 (cs.CL) 21 Feb 2017
 X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in International Conference on Artificial Intelligence and Statistics, 05 2010, pp. 249–256
 G. Kumar, G. W. Blackwood, J. Trmal, D. Povey, and S. Khudanpur, “A coarse-grained model for optimal coupling of ASR and SMT systems for speech translation.” in Proceedings of EMNLP, 2015, pp. 1902–1907.
 E. Vidal, “Finite-state speech-to-speech translation,” in Proceedings of ICASSP, vol. 1. IEEE, 1997.
 F. Casacuberta, H. Ney, F. J. Och, E. Vidal, J. M. Vilar, S. Barrachina, I. Garcıa-Varea, D. Llorens, C. Martınez, S. Molau et al., “Some approaches to statistical and finite-state speech-to-speech translation,” in Computer Speech & Language, vol. 18, no. 1, pp. 25–47, 2004.