Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning
نویسندگان
چکیده
منابع مشابه
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Sequence-to-sequence (Seq2seq) models have played an import role in the recent success of various natural language processing methods, such as machine translation, text summarization, and speech recognition. However, current Seq2seq models have trouble preserving global latent information from a long sequence of words. Variational autoencoder (VAE) alleviates this problem by learning a continuo...
متن کاملSequence to Sequence Learning in Neural Network
Neural Network Elements. Deep learning is the name we use for “stacked neural networks”; that is, networks composed of several layers. The layers are made of nodes. A node is just a place where computation happens, loosely patterned on a neuronin the human brain, which fires when it encounters sufficient stimuli. Deep Neural Networks (DNNs) are powerful models that have achieved excellent perfo...
متن کاملRecurrent Neural Network-based Tuple Sequence Model for Machine Translation
In this paper, we propose a recurrent neural network-based tuple sequence model (RNNTSM) that can help phrase-based translation model overcome the phrasal independence assumption. Our RNNTSM can potentially capture arbitrary long contextual information during estimating probabilities of tuples in continuous space. It, however, has severe data sparsity problem due to the large tuple vocabulary c...
متن کاملRecurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping
We introduce an encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks. Like connectionist temporal classification (CTC) models, RNA defines a probability distribution over target label sequences including blank labels corresponding to each time step in input. The probability of a label sequence is calculated ...
متن کاملArtificial Neural Network for Sequence Learning
This poster shows an artificial neural network capable of learning a temporal sequence. Directly inspired from a hippocampus model [Banquet et al, 1998], this architecture allows an autonomous robot to learn how to imitate a sequence of movements wi th the correct t iming.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Sciences
سال: 2019
ISSN: 0020-0255
DOI: 10.1016/j.ins.2019.03.066