PPT-Addressing the Rare Word Problem in Neural Machine Translat

Minh Tang Luon Stanford University Iiya Sutskever Google Quoc VLe Google Orial Vinyals Google Wojciech Zaremba New York Univerity Abstract Neural Machine Translation NMT is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. Chih. -Hung Wang. Chapter 1: Background (Part-1). 參考書目. Leland . L. . Beck. , System Software. : An . Introduction to Systems . Programming (3rd), Addison-Wesley, 1997.. 1. Outline of Chapter 1. and parallel corpus generation. Ekansh. Gupta. Rohit. Gupta. Advantages of Neural Machine Translation Models. Require . only a fraction of the memory needed by traditional statistical machine translation (SMT) . Chong Ho Yu. What is data mining?. Data mining (DM) is a cluster of techniques, including decision trees, artificial neural networks, and clustering, which has been employed in the field Business Intelligence (BI) for years.. Thang. . Luong. . Joint work with . Richard . Socher. . and . Christopher D. Manning. Word frequencies in Wikipedia documents . (986 million tokens). And more … . indistinctly. , non-distinct, indistinctive, non-distinctive, indistinctness, . N machines. Each may break down and join the repair’s man queue. Operation time . Exponentially distributed with rate . λ. Repair time. Exponentially distributed with rate . μ. N. machines. Repair’s man queue. Omid Kashefi. omid.Kashefi@pitt.edu. Visual Languages Seminar. November, 2016. Outline. Machine Translation. Deep Learning. Neural Machine Translation. Machine Translation. Machine Translation. Use of software in translating from one language into another. Gap. . between . Human and Machine Translation. Wu. . et. al., . arXiv. - . sept. 2016. Presenter. : Lütfi Kerem Şenel. Outline. . Introduction. . and. . Related. . w. orks. . Model Architecture . Machine . Translation. . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . Department of Computer Science and Information Engineering. National Cheng Kung University, Tainan Taiwan. Tsung-Wei Huang, Tsung-Yi Ho, and Krishnendu Chakrabarty. Department of Electrical and Computer Engineering. . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . Gary Cottrell. Computer Science and Engineering Department. Institute for Neural Computation. Temporal Dynamics of Learning Center. UCSD. 4/11/17. CSE 87. 2. Introduction. Your brain is made up of 10. . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.. . Thang . Luong. ACL 2015. Joint work with. : . Ilya Sutskever. , . Quoc . Le. , . Oriol Vinyals. , . & . Wojciech. . Zaremba. .. Standard Machine Translation (MT). T. ranslate . locally phrases by phrases: . May.  4. , 20. 21. Junjie Hu. Materials largely borrowed from Austin Matthews. One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: .