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A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
TLDR
We propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents. Expand
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Learning semantic representations using convolutional neural networks for web search
TLDR
This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. Expand
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Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
TLDR
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. Expand
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ReasoNet: Learning to Stop Reading in Machine Comprehension
TLDR
ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Expand
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Stochastic Answer Networks for Machine Reading Comprehension
TLDR
We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Expand
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FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
TLDR
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. Expand
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End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
TLDR
We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document. Expand
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M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
TLDR
We develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). Expand
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Language-Based Image Editing with Recurrent Attentive Models
We investigate the problem of Language-Based Image Editing (LBIE). Given a source image and a natural language description, we want to generate a target image by editing the source image based on theExpand
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Semantic Modelling with Long-Short-Term Memory for Information Retrieval
In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methodsExpand
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