[PDF] Top 20 Decomposable Neural Paraphrase Generation
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Decomposable Neural Paraphrase Generation
... First, we evaluate our model quantitatively in terms of automatic metrics such as BLEU (Pap- ineni et al., 2002), ROUGE (Lin, 2004), which have been widely used in previous works on para- phrase generation. In ... See full document
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Controllable Paraphrase Generation with a Syntactic Exemplar
... text generation usu- ally assumes that the controlled attribute can take on one of a small set of values known a ...a neural module specifi- cally designed for capturing syntactic knowl- edge and several ... See full document
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Exploring Diverse Expressions for Paraphrase Generation
... deep neural models have also been applied to paraphrase generation due to their great success on natural language processing ...controlled paraphrase, Li et ...text generation A few ... See full document
10
Transformer and seq2seq model for Paraphrase Generation
... Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, et al. 2018. The best of both worlds: Combining recent advances in ... See full document
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Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets
... of paraphrase generation has many im- portant applications in ...train neural networks so that they become less susceptible to adversarial attack (Iyyer et ...incorporating paraphrase ... See full document
9
Paraphrase Generation with Deep Reinforcement Learning
... In this work, we propose taking a data-driven approach to train a model that can conduct evalu- ation in learning for paraphrasing generation. The framework contains two modules, a generator (for paraphrase ... See full document
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An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
... prediction ever since it was proposed in (Bengio et al., 2015). Similar to IL, reinforcement learn- ing, particularly with neural network models, has been widely used in many different domains, such as coreference ... See full document
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Neural Paraphrase Identification of Questions with Noisy Pretraining
... the decomposable attention model to use sums of character n-gram embeddings instead of word ...complex neural ar- chitectures, all using pretrained word ... See full document
6
Measuring Semantic Abstraction of Multilingual NMT with Paraphrase Recognition and Generation Tasks
... gual neural translation models learn stronger semantic abstractions of sentences than bilin- gual ...on paraphrase generation that we also include at the end of the ... See full document
8
Paraphrase Generation for Semi Supervised Learning in NLU
... that neural machine transla- tion (NMT) systems perform better than phrase- based MT systems in paraphrase generation ...phrase generation using the transformer leads to better performance ... See full document
10
Negative Lexically Constrained Decoding for Paraphrase Generation
... However, paraphrase generation does not require rewriting of all ...a neural model for paraphrase generation that first identifies words in the source sentence requiring ... See full document
6
A Task in a Suit and a Tie: Paraphrase Generation with Semantic Augmentation
... Recent neural paraphrasing systems (Prakash et al. 2016; Gupta et al. 2018) adopt automatic evaluation measures commonly used in MT, citing good correlation with human judgment (Madnani and Tetreault 2010; Wubben, ... See full document
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Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
... a paraphrase of the sentence with the de- sired ...a neural encoder- decoder model with extra inputs to specify the target ...decreasing paraphrase qual- ity when compared to baseline (uncontrolled) ... See full document
11
Neural Paraphrase Generation using Transfer Learning
... statistical paraphrase generation has been hindered for a long time by the lack of large monolingual parallel ...the neural machine translation approach to paraphrase generation and ... See full document
5
Neural Clinical Paraphrase Generation with Attention
... lexical paraphrase extraction task. Our work is the first to propose a neural network-based architecture that can model word/character sequences to essentially address all granularities of paraphrase ... See full document
12
Neural Paraphrase Generation with Stacked Residual LSTM Networks
... Applications of deep learning for paraphrase generation tasks have not been rigorously explored. We utilized several sources as potential large datasets. Recently, Weiting et al. (2015) took the PPDB ... See full document
12
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks
... Metaphor is an increasingly studied phenomenon in computational linguistics. But while metaphor detection has received considerable attention in the NLP literature (Dunn et al., 2014; Veale et al., 2016) and in corpus ... See full document
11
Paraphrase Generation from Latent Variable PCFGs for Semantic Parsing
... for paraphrase identification, and experiment with 8 MT metrics as features for our binary ...sampled paraphrase preserves named entities from the input ...the paraphrase al- gorithm is used for ... See full document
10
An Empirical Evaluation of Data Driven Paraphrase Generation Techniques
... proposed paraphrase gen- eration techniques, which range from very simple approaches that make use of little-to-no NLP or language-dependent resources to more sophisticated ones that heavily rely on such ... See full document
6
Creating and using large monolingual parallel corpora for sentential paraphrase generation
... of the data in the DAESO corpus consists of headline clus- ters scraped from Google News in the period April–August 2006. Google News uses clustering algorithms that con- sider the full text of each news article, as well ... See full document
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