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

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. Problem with regular RNNs. The standard learning algorithms for RNNs don’t allow for long time lags. Problem: error signals going “back in time” in BPTT, RTRL, . etc. either exponentially blow up or (usually) exponentially vanish. Aurora 8B/10B v11.0 www.xilinx.com PG046 October 5, 2016 Table of ContentsChapter1:OverviewApplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conference on Empirical Methods in Natural Language . Processing 2007. 報告者：郝柏翰. 2013/06/04. Thorsten . Brants. , Ashok . C. . Popat. , . Peng. . Xu. , Franz . J. . Och. , Jeffrey Dean. Introduction to MT. Machine Translation. Fully automatic. Helping human translators. Enter Source Text:. Translation from Stanford’s . Phrasal. :.  这 不过 是 一 个 时间 的 问题 . .. This is only a matter of time.. (it’s all about the readers). Mikel L. . Forcada. Universitat. . d’Alacant. (Spain). Prompsit. Language Engineering (. Elx. , Spain). European Association for Machine Translation. All translation is communication. Arun . Mallya. Best viewed with . Computer Modern fonts. installed. Outline. Why Recurrent Neural Networks (RNNs)?. The Vanilla RNN unit. The RNN forward pass. Backpropagation. refresher. The RNN backward pass. Gap. . between . Human and Machine Translation. Wu. . et. al., . arXiv. - . sept. 2016. Presenter. : Lütfi Kerem Şenel. Outline. . Introduction. . and. . Related. . w. orks. . Model Architecture . . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . . (CVPR. . 2015). Presenters:. . Tianlu. . Wang. ,. . Y. i. n . Zhang . Oct. ober. 5. th. Human: A young girl asleep on the sofa cuddling a stuffed bear.. NIC: A baby is asleep next to a teddy bear.. 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 Machine Translation. Fully automatic. Helping human translators. Enter Source Text:. Translation from Stanford’s . Phrasal. :.  这 不过 是 一 个 时间 的 问题 . .. This is only a matter of time.. to apply an ap-Our analysis with patience and helpful and getting led me me from 1950W ork Models Are Choosing a Conclusions and D A ei eto the score in language model model 693-2 Find the opti Neural Engineering Data Consortium. Temple University. EEG Segments. Kaldi Adaptation for EEG event classification. Outline. Introduction to EEGs and various seizure morphologies. Seizure data and feature extraction.