[PDF] Top 20 Attention Strategies for Multi Source Sequence to Sequence Learning
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Attention Strategies for Multi Source Sequence to Sequence Learning
... The attention mechanism in S2S learning allows an RNN decoder to directly access information about the input each time before it emits a sym- bol. Inspired by content-based addressing in Neu- ral Turing ... See full document
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Learning to Copy for Automatic Post Editing
... two-source sequence-to-sequence learning problem in which both src and mt play important ...major source for generating pe since many words ...in multi-source ... See full document
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A Study on Personalized Travel Sequence Recommendation on Multi-Source Social Media
... travel sequence recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different ...a sequence of travel interest instead of an individual ... See full document
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An Analysis of Active Learning Strategies for Sequence Labeling Tasks
... Active learning is well-suited to many prob- lems in natural language processing, where unlabeled data may be abundant but annota- tion is slow and ...active learning ap- proaches for sequence ... See full document
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Sequence Classification with Human Attention
... training multi-task architectures (Dong et ...the attention weights assigned to the input ...normalized attention score. In other words, in multi-task learning, we optimize each task ... See full document
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Look Harder: A Neural Machine Translation Model with Hard Attention
... entire source sequence for each target token to effectively handle long sequence ...ment Learning (RL) algorithm with reward shap- ing strategy (Ng et ...the sequence length ... See full document
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Multi Modal Sequence Fusion via Recursive Attention for Emotion Recognition
... Most approaches to multi-modal analysis (Ngiam et al., 2011) focus on designing feature repre- sentations, co-learning mechanisms to transfer in- formation between modalities, and fusion tech- niques to ... See full document
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An Eye Movement Study on the Relationship Between Multiple Implicit Sequence Learning and Attention
... implicit sequence learning requires attention ...implicit sequence learning, even subtle changes in the experimental setups and research designs might have provoked differences in the ... See full document
6
Sequence to Sequence Learning as Beam Search Optimization
... some attention as a way to test the (syntactic) capa- bilities of text-generation systems (Zhang and Clark, 2011; Zhang and Clark, 2015; Liu et ...a source sentence, and the correctly ordered sentence as ... See full document
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Unsupervised Pretraining for Sequence to Sequence Learning
... predict the entire summary. Only one language model is trained and it is used to initialize both the encoder and decoder, since the source and tar- get languages are the same. However, the encoder and decoder are ... See full document
9
Learning Multi-Task Communication with Message Passing for Sequence Learning
... non-neural multi-task learning methods have also been proposed to model the relationships between ...structured multi-task problem over a given ...complex learning strategies and ... See full document
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Sequence to Sequence Learning for Event Prediction
... • We explore multi-layer RNNs which have currently shown the advantage over sin- gle/shallow RNNs (Sutskever et al., 2014; Vinyals et al., 2015; Luong et al., 2015). We use a bidirectional RNN architecture for the ... See full document
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Exploring Sequence to Sequence Learning in Aspect Term Extraction
... Seq2Seq learning, attention mechanism is used to make the decoder select important parts of source se- quence to form a context vector for decoding cur- rent word (Bahdanau et ...of attention ... See full document
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Bandit Structured Prediction for Neural Sequence to Sequence Learning
... dit learning objectives for structured prediction and apply them to various NLP tasks, including machine translation with linear ...inforcement learning to one-state Markov deci- sion processes under linear ... See full document
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Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network
... for multi-scale local regions individually and aggre- gates these features by concatenation, where the two steps are repeated to complete the hierarchical feature ... See full document
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Paragraph level Neural Question Generation with Maxout Pointer and Gated Self attention Networks
... Answer-aware sequence to sequence neural QG systems (Zhou et ...reinforcement learning in the training of their model using policy gradi- ent techniques to maximize several rewards that measure ... See full document
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Self Regulated Interactive Sequence to Sequence Learning
... Training. The Seq2Seq model is first trained on WMT with Adam (Kingma and Ba, 2015) on mini-batches of size 64, an initial learning rate 1 × 10 −4 that is halved when the loss does not de- crease for three ... See full document
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Multimodal Transformer Networks for End to End Video Grounded Dialogue Systems
... the source sequences, including dialogue history, video caption, and query, into a single sequence and use only one multi-head attention block on this concatenated sequence in each ... See full document
12
Sequence-to-sequence modeling for graph representation learning
... LSTM sequence-to-sequence learning framework of (Sutskever et ...input sequence into a vector and another LSTM to generate the output sequence from that ...same sequence as both ... See full document
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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
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