[PDF] Top 20 Multilingual Hierarchical Attention Networks for Document Classification
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Multilingual Hierarchical Attention Networks for Document Classification
... The multilingual model trained on pairs of lan- guages outperforms on average all the examined monolingual models, namely a bag-of-word neu- ral model and two hierarchical neural models which use average ... See full document
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Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations
... Multi-aspect sentiment analysis. This task usu- ally requires aspect segmentation, followed by prediction or summarization (Hu and Liu, 2004; Zhuang et al., 2006). Most related studies have engineered various feature ... See full document
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Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
... neural networks that work on each context sub- sequence ...a hierarchical recurrent neural network, which firstly learns the context representation of each context subsequence independently, and then ... See full document
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Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization
... We compared the above models with the model without any discourse-aware attention mecha- nisms (no-attn) to verify the effectiveness of our attention mechanisms. Lead-3 is a com- mon baseline to select the ... See full document
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An Improved Hierarchical Bayesian Model of Language for Document Classification
... This paper addresses the fundamental problem of document classification, and we focus attention on classification prob- lems where the classes are mutually exclu- sive. In the course of the ... See full document
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Initializing neural networks for hierarchical multi label text classification
... a hierarchical structure (such as a ...(OVR) classification setup, where a binary clas- sifier is trained for each label in the tax- onomy or ontology where all instances not belonging to the class are ... See full document
9
Topic Spotting using Hierarchical Networks with Self Attention
... based document classification ...based document classification ...in document classification (5/6 classes), which is done mainly on the texts from customer ... See full document
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Discourse Parsing with Attention based Hierarchical Neural Networks
... A document is formed by a series of coherent text units. Document-level discourse parsing is a task to identify the relations between the text units and to determine the structure of the whole ... See full document
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A Dual Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
... class classification (e.g., SVMs (Liu, 2006) and dynamic Bayesian networks (Dielmann and Re- nals, 2008)) and structured prediction tasks includ- ing HMM (Stolcke et ...DA classification with a model ... See full document
10
Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature
... the hierarchical indexing ...enrich document representation from the corresponding categories and ...the document together with its ...the document (shown in ... See full document
9
Multilingual Multi class Sentiment Classification Using Convolutional Neural Networks
... Convolutional Neural Networks (CNNs) (LeCun et al., 1995) are a powerful deep learning technique because they preserve the spatial structure of the data. They have been shown to produce state-of-the-art results in ... See full document
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Hierarchical Multi label Classification of Text with Capsule Networks
... capsule networks to several baseline neu- ral as well as non-neural architectures on the BlurbGenreCollection (BGC), a dataset which we collected and that consists of so-called blurbs of books and their ... See full document
8
Sentence Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
... More recently, semantic matching methods were proposed to retrieve evidence from relatively trustworthy sources such as checked news and Wikipedia articles. Popat et al. (2018) attempted to debunk false claims by ... See full document
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Hashtag Recommendation Using End To End Memory Networks with Hierarchical Attention
... much attention in recent ...memory networks to perform this ...a hierarchical attention mechanism to select more appropriate ...the hierarchical attention mechanism, the relative ... See full document
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Cross Target Stance Classification with Self Attention Networks
... First, selecting the effective source targets to generalize from is crucial for achieving satisfying results on the destination targets. One possibil- ity could be to learn certain correlations between target closeness ... See full document
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Hierarchical Attention Networks for Sentence Ordering
... Two typical tasks are commonly used to build and evalu- ate models that understand coherence. One is a discrimina- tion task that identifies the more coherent ordering given a document and a permuted version of ... See full document
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Hierarchical Convolutional Attention Networks for Text Classification
... RNN-based approaches for text processing can in- herently account for word order when extracting features. However, feedforward and convolution- based approaches such as our implementation of convolutional multihead ... See full document
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PRADO: Projection Attention Networks for Document Classification On Device
... text classification relied on sparse lexical features such as n-grams and linear clas- sifiers (Joachims, 1998; McCallum and Nigam, 1998; Joulin et ...a document vector. Re- cently, (Yang et al., 2016) ... See full document
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Hierarchical Attention Prototypical Networks for Few Shot Text Classification
... erarchical attention prototypical networks consist- ing of feature level, word level and instance level multi cross attention, which highlight the impor- tant information of few data and learn a more ... See full document
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Hierarchical Attention Networks for Document Classification
... a hierarchical attention network for document ...the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the word- and sentence-level, ... See full document
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