[PDF] Top 20 Sequence Classification with Human Attention
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Sequence Classification with Human Attention
... Note again that our architecture does not require the target task data to come with eye-tracking in- formation. We instead learn jointly to predict sen- tence categories and to attend to the tokens hu- mans tend to focus ... See full document
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Hierarchically Refined Label Attention Network for Sequence Labeling
... Neural Attention. Attention has been shown useful in neural machine translation (Bahdanau et ...sentiment classification (Chen et ...text classification (Xu et ...for sequence repre- ... See full document
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Hierarchical Convolutional Attention Networks for Text Classification
... Text classification is an important research area in natural language processing ...text classification approaches utilize features gen- erated from vector space models such as bag-of- words or term ... See full document
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Multilingual Hierarchical Attention Networks for Document Classification
... hierarchical attention networks for document classification and showed that they can benefit both full-resource and low- resource settings, while using fewer parameters than monolingual ...the ... See full document
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Dilated LSTM with attention for Classification of Suicide Notes
... is little evidence which features are most rele- vant for the accurate classification. Therefore we firstly analyse the most important linguistic fea- tures in suicide notes, depressed notes and last statements. ... See full document
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Molecular Evolution of the Human Enteroviruses: Correlation of Serotype with VP1 Sequence and Application to Picornavirus Classification
... To determine the phylogenetic relationships among individ- ual prototype viruses and, where available, prototypes and their antigenic variants, intracluster phylogenetic trees were constructed with several phylogeny ... See full document
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Convolution based neural attention with applications to sentiment classification
... an attention mechanism to conventional NN-based models. NN with attention is able to attend to specific parts of text as the simulation of human’s attention while processing ...The attention ... See full document
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Relation Classification via Multi Level Attention CNNs
... relation classification has been supervised, typically cast as a standard multi- class or multi-label classification ...sub- sequence kernels (Mooney and Bunescu, 2005), or dependency tree kernels ... See full document
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Generating Video Description using Sequence to sequence Model with Temporal Attention
... Since the recent breakthrough in machine learning, generating description for static images has been intensively researched and high-quality image description can be achieved in the past few years by many research groups ... See full document
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Dialogue Act Classification with Context Aware Self Attention
... the classification accuracy of our model against several other recent methods (Ta- ble ...use attention in some form to model the con- versations, but none of them have explored self- attention for ... See full document
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Deep Short Text Classification with Knowledge Powered Attention
... text classification is one of important tasks in Natu- ral Language Processing ...introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered At- tention ...ST) ... See full document
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NLP at IEST 2018: BiLSTM Attention and LSTM Attention via Soft Voting in Emotion Classification
... (RNN), to be exact, we respectively examine L- STM and Bidirectional LSTM (Zeng et al., 2016) to process the tweets. LSTM firstly introduced by (Hochreiter and Schmidhuber, 1997) has proven to be stable and powerful for ... See full document
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Paragraph level Neural Question Generation with Maxout Pointer and Gated Self attention Networks
... QG has been mainly tackled with two types of ap- proaches. One is built on top of heuristic rules that creates questions with manually constructed tem- plate and ranks the generated results, e.g. (Heil- man and Smith, ... See full document
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Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations
... like attention-based neural net- works (Luong et ...the attention is modeled by a normalized exponential function, namely a softmax and a linear activation between a contextual vector and the doc- ument ... See full document
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Iterative Recursive Attention Model for Interpretable Sequence Classification
... iterative attention mechanism (Sordoni et ...recursive attention model (IRAM), where the result of an attentive query is nonlinearly transformed and then added to the set of vector representations of the ... See full document
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Attention Strategies for Multi Source Sequence to Sequence Learning
... We used the data from the WMT16 APE Task (Bojar et al., 2016; Turchi et al., 2016), which consists of 12,000 training, 2,000 validation, and 1,000 test sentence triplets from the IT domain. Each triplet contains an ... See full document
7
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks
... the attention weights for the text is calculated with AOA ...to-sentence attention and sentence-to-target atten- ...sentence-level attention is calcu- lated by a weighted sum of each individual ... See full document
7
Cross Target Stance Classification with Self Attention Networks
... An alternative to this approach is to conduct a cross-target classification, where the classifier is adapted from different but related targets (Au- genstein et al., 2016), which allows benefiting from the ... See full document
6
Hierarchical Attention Networks for Document Classification
... Figure 5 shows that our model can select the words carrying strong sentiment like delicious, amazing, terrible and their corresponding sentences. Sentences containing many words like cocktails, pasta, entree are disre- ... See full document
10
Recurrent Residual Learning for Sequence Classification
... Identity connections in ResNet are important for propagating the single input image information to higher layers of CNN. However, when it comes to sequence classification, the scenario is quite differ- ent ... See full document
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