PPT-Ch 10b. Sequence to sequence model based using LSTM for machine translation

Author : conchita-marotz | Published Date : 2019-03-19

KH Wong RNN LSTM and sequencetosequence model v8b 1 Introduction Neural Machine translation Learn by training Eg EnglishFrench translator development Need a lot

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Ch 10b. Sequence to sequence model based using LSTM for machine translation: Transcript

KH Wong RNN LSTM and sequencetosequence model v8b 1 Introduction Neural Machine translation Learn by training Eg EnglishFrench translator development Need a lot of English fence sentence pairs as training data. Minh Tang . Luon. (Stanford University). Iiya. . Sutskever. (Google). Quoc. . V.Le. (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. l Networks. Presente. d by:. Kunal Parmar. UHID: 1329834. 1. Outline of the presentation . Introduction. Supervised Sequence Labelling. Recurrent Neura. l Networks. How can RNNs be used for supervised sequence labelling?. Srivastava,. Elman . Mansimov. ,. Ruslan. . Salakhutdinov. ,. University of Toronto. Unsupervised Learning of Video Representations using LSTMs . Agenda. Quick Intro. Supervised vs. Unsupervised. Problem Definition. 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. . Example Application. Slot Filling. I would like to arrive . Taipei . on . November 2. nd. .. . ticket booking system. Destination:. time of arrival:. Taipei. November 2. nd. . Slot. Example Application. Li Deng . Deep Learning Technology Center. Microsoft AI and Research Group. Invited Presentation at NIPS Symposium, December 8, 2016. Outline. Topic one. : RNN versus Nonlinear Dynamic Systems;. sequential discriminative vs. generative models. . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . Features: . (. i. ) Provides standardised ‘. DeepSEA. score’ for noncoding variants. (ii) Provides info on chromatin feature(s) and cell type(s) to concentrate on. (iii) Identify base-resolution sequence features by . Probabilistic Graphical Models. Prof. Adriana . Kovashka. University of Pittsburgh. November 27, 2018. Plan for This Lecture. Motivation for probabilistic graphical models. Directed models: Bayesian networks. SYNTAX BASED MACHINE TRANSLATION UNDER GUIDANCE OF PROF PUSHPAK BHATTACHARYYA PRESENTED BY ROUVEN R Ӧ HRIG (10V05101) ERANKI KIRAN (10438004) SRIHARSA MOHAPATRA (10405004) ARJUN ATREYA (09405011) 9/4/2011 Mark Ragan. Institute for Molecular Bioscience. and. . School of Information Technology & Electrical Engineering. The University of Queensland, Brisbane, Australia. IPAM Workshop on Multiple Sequence Alignment. Secondary structures. Tertiary structures. MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE. helices. strands. loops. Three dimensional packing of secondary structures. Protein Structures. Protein structures.

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