[PDF] Top 20 Sequence to Sequence Learning with Neural Networks
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Sequence to Sequence Learning with Neural Networks
... general sequence to sequence ...input sequence, one timestep at a time, to obtain large fixed- dimensional vector representation, and then to use another LSTM to extract the output sequence ... See full document
9
Self Regulated Interactive Sequence to Sequence Learning
... tive sequence-to-sequence learning, with a self- regulation module at its core that learns which type of feedback to query from a human ...models learning from a single feedback type and ... See full document
13
Bandit Structured Prediction for Neural Sequence to Sequence Learning
... non-linear sequence-to-sequence learning us- ing recurrent neural networks with ...of neural machine translation domain adaptation, we found relative improvements of up to ... See full document
11
Graph to Sequence Learning using Gated Graph Neural Networks
... graph-to-sequence learning problem. Previous work proposing neural architec- tures on this setting obtained promising results compared to grammar-based ap- proaches but still rely on linearisation ... See full document
11
Pervasive Attention: 2D Convolutional Neural Networks for Sequence to Sequence Prediction
... Deep neural networks have made a profound im- pact on natural language processing technology in general, and machine translation in particular (Blunsom, 2013; Sutskever et ...a sequence- ... See full document
11
Implicitly Defined Neural Networks for Sequence Labeling
... Feedforward neural networks were designed to ap- proximate and interpolate ...Recurrent Neural Networks (RNNs) were developed to pre- dict ...feedforward networks, with weights shared ... See full document
6
Still not there? Comparing Traditional Sequence to Sequence Models to Encoder Decoder Neural Networks on Monotone String Translation Tasks
... 5.3, neural models are only able to successfully compete when more complex phenomena occur, on which traditional models ...complex sequence labeling tasks such as spoken language understanding, ... See full document
12
Evaluation of Sequence Learning Models for Large Commercial Building Load Forecasting
... of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load ...Recurrent Neural Network ... See full document
14
Efficient Sequence Learning with Group Recurrent Networks
... for sequence learning is a hot topic in recent deep learning ...delta networks for op- timizing the matrix-vector multiplications in RNN computation by considering the temporal proper- ties of ... See full document
10
Neural Sequence Learning Models for Word Sense Disambiguation
... Several recent contributions (Søgaard and Gold- berg, 2016; Bjerva et al., 2016; Plank et al., 2016; Luong et al., 2016) have shown the effectiveness of multitask learning (Caruana, 1997, MTL) in a sequence ... See full document
12
Contextualized Non-Local Neural Networks for Sequence Learning
... Attention-based Sequence Modelling. Several attention- based mechanisms have been introduced to expand the inter- action scope, enabling more words to interact with each to- gether. For example, Yang et al., Lin ... See full document
8
Neural Sequence to sequence Learning of Internal Word Structure
... a neural encoder-decoder model for learn- ing canonical morphological segmenta- ...character-level sequence-to-sequence transformation with a language model over canonical seg- ... See full document
11
Sequence to Sequence Learning as Beam Search Optimization
... and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word ...different sequence to sequence tasks: word ... See full document
11
Unsupervised Pretraining for Sequence to Sequence Learning
... Despite its appeal, unsupervised learning has not been widely used to improve supervised train- ing. Dai and Le (2015); Radford et al. (2017) are amongst the rare studies which showed the ben- efits of pretraining ... See full document
9
Incorporating Copying Mechanism in Sequence to Sequence Learning
... In this paper, we explore another mechanism important to the human language communication, called the “copying mechanism”. Basically, it refers to the mechanism that locates a certain seg- ment of the input sentence and ... See full document
10
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
... machine learning methods, which re- ly on exploring various features to represent QA ...answer sequence for the semantic matching between question and ... See full document
6
Sequence to Sequence Learning for Event Prediction
... The training objective is to minimize the cross- entropy loss using the Adam optimizer (Kingma and Ba, 2015) and a mini-batch size of 64. The initial learning rate for Adam is selected from {0.0001, 0.0005, 0.001, ... See full document
6
Simple Models for Word Formation in Slang
... of learning to blend and derive data-driven com- putational models for the ...length sequence to sequence learning problem and propose a neural encoder-decoder based ... See full document
11
Identifying beneficial task relations for multi task learning in deep neural networks
... Contributions This paper presents a systematic study of when and why MTL works in the context of sequence labeling with deep recurrent neural networks. We follow previous work (Klerke et al., 2016; ... See full document
6
Efficient Convolutional Neural Networks for Diacritic Restoration
... a sequence labeling problem and cur- rently Bidirectional Long Short Term Mem- ory (BiLSTM) models provide state-of-the-art ...ral Networks (TCN) over Recurrent Neural Networks (RNN) for ... See full document
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