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A Neural Attention Model for Abstractive Sentence Summarization
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach toExpand
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Learning a similarity metric discriminatively, with application to face verification
We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known duringExpand
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Sequence Level Training with Recurrent Neural Networks
Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and someExpand
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Dimensionality Reduction by Learning an Invariant Mapping
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold.Expand
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Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progressExpand
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Memory Networks
Abstract: We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use theseExpand
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The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic functionExpand
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Large-scale Simple Question Answering with Memory Networks
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitaskExpand
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Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generatesExpand
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Efficient Learning of Sparse Representations with an Energy-Based Model
We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a codeExpand
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