[PDF] Top 20 Sentence Simplification with Memory Augmented Neural Networks
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Sentence Simplification with Memory Augmented Neural Networks
... Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for hu- man readers, and easier to process for down- stream NLP ...in ... See full document
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EditNTS: An Neural Programmer Interpreter Model for Sentence Simplification through Explicit Editing
... for sentence simplifica- tion; Woodsend and Lapata (2011) propose a quasi-synchronous grammar and use integer lin- ear programming to score the simplification rules; Wubben et ...performs sentence ... See full document
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GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
... Graph Augmented Memory Networks (GAMENet), an end-to-end deep learning model that takes both longitudinal patient EHR data and drug knowledge base on DDIs as inputs and aims to generate effective and ... See full document
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Learning Word Representations with Cross Sentence Dependency for End to End Co reference Resolution
... short-term memory (LSTM) re- current neural networks on each sentence of an input article or conversation separately, we propose linear sentence linking and atten- tional ... See full document
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Rationale Augmented Convolutional Neural Networks for Text Classification
... Table 2: Accuracies on the four RoB datasets. Uni-SVM: unigram SVM, Bi-SVM: Bigram SVM, RA-SVM: Rationale-augmented SVM (Zaidan et al., 2007), MT-SVM: a multi-task SVM model specifically designed for the RoB task, ... See full document
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Sentence Simplification with Deep Reinforcement Learning
... In this paper we propose a simplification model which draws on insights from neural machine translation (Bahdanau et al., 2015; Sutskever et al., 2014). Central to this approach is an encoder- decoder ... See full document
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Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
... For the decoder we experimented with both the Elman RNN and the Long-Short Term Memory (LSTM) architecture (as discussed in § 3.1). We chose hyper-parameters based on a grid search and picked the one which gave ... See full document
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Memory augmented Neural Machine Translation
... Despite positive results obtained so far, a par- ticular problem of the NMT approach is that it has a tendency towards overfitting to frequent ob- servations (words, word co-occurrences, transla- tion pairs, etc.), but ... See full document
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Neural Networks for Semantic Textual Similarity
... two sentence embedding stacks’ sequences ...which neural networks with memory are bet- ter at ...a sentence whose meanings can learned and composed to obtain the unique meaning of a ... See full document
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Holographic Generative Memory: Neurally Inspired One-Shot Learning with Memory Augmented Neural Networks
... Hopfield networks can have potential as an auto-associative cleanup memory, but their capacity related to vector size limits their ...recurrent neural networks (RNNs) like Long Short-Term ... See full document
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Memory Augmented Neural Networks for Machine Translation
... Memory-augmented neural networks (MANNs) have been shown to outper- form other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have ... See full document
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Research on attention memory networks as a model for learning natural language inference
... on neural networks for text similarity tasks includ- ing NLI have been published in recent years (Hu et ...deep sentence encoding model- s, for example, with convolutional networks (LeCun et ... See full document
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Character Based Neural Networks for Sentence Pair Modeling
... 2 Sentence Pair Modeling with Subwords The current neural networks for sentence pair modeling (Yin et ...multiple sentence pair modeling tasks and the best results by neural mod- ... See full document
7
Unsupervised Sentence Simplification Using Deep Semantics
... Sentence simplification maps a sentence to a sim- pler, more readable one approximating its ...2014), sentence simplification has many potential ...2000), sentence fusion ... See full document
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Dependency based Convolutional Neural Networks for Sentence Embedding
... Powerful as it is, structural information still does not fully cover sequential information. Also, pars- ing errors (which are common especially for in- formal text such as online reviews) directly affect DTCNN ... See full document
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EASSE: Easier Automatic Sentence Simplification Evaluation
... We introduce EASSE, a Python package aim- ing to facilitate and standardise automatic evaluation and comparison of Sentence Sim- plification (SS) systems. EASSE provides a single access point to a broad range of ... See full document
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Exploring Verb Frames for Sentence Simplification in Hindi
... We present a rule based system for sentence sim- plification in Hindi. Our evaluation results show an average readability of 1.85 in the scale of 0-3, while 2.07 on the scale of 0-3 in system perfor- mance on ... See full document
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EASSE: Easier Automatic Sentence Simplification Evaluation
... SAMSA measures structural simplicity (i.e. sen- tence splitting). This is in contrast to SARI, which is designed to evaluate simplifications involv- ing paraphrasing. EASSE re-factors the original SAMSA implementation 2 ... See full document
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Crowdsourced Corpus of Sentence Simplification with Core Vocabulary
... Major phenomena of simplification in the evaluation cor- pus is shown in Table 1. The same optimal word may not always be substituted, because the annotator may not know all 2,000 words and there are multiple ways ... See full document
6
Entity Focused Sentence Simplification for Relation Extraction
... For sentence simplification in relation extrac- tion, the meaning of the target sentence itself is less important than maintaining the truth-value of the relation (interact or ...simpler ... See full document
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