[PDF] Top 20 Syntax Infused Variational Autoencoder for Text Generation
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Syntax Infused Variational Autoencoder for Text Generation
... Neural Text Generation The ability to generate sentences is core to many NLP tasks, such as machine translation (Bahdanau et ...alogue generation (Vinyals and Le, ...neural text genera- tion ... See full document
10
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation
... It has been observed that VAEs tend to suffer from the posterior collapse issue, especially when pow- erful autoregressive decoders are used for model- ing text sequences. A common solution is to warm up the KL ... See full document
11
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
... boosting autoencoder with variational infer- ence (Kingma and Welling, 2014), known as vari- ational autoencoder (VAE), can generate not only consistent but also novel and fluent term sequences ... See full document
11
On the Importance of the Kullback Leibler Divergence Term in Variational Autoencoders for Text Generation
... generated text: the decoder trained under smaller KL term tends to generate repetitive but mainly plausible sentences, while for larger KL the generated sentences were diverse but inco- ... See full document
10
Topic Guided Variational Auto Encoder for Text Generation
... using less conditional information while generating each word) (Yang et al., 2017; Shen et al., 2017a), or bridging the amortization gap (between the log- likelihood and the ELBO) using semi-amortized inference networks ... See full document
12
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
... The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaus- sian) on the latent space and penalizes the Kullback–Leibler (KL) divergence between the posterior and ...for ... See full document
9
Learning to Write Stories with Thematic Consistency and Wording Novelty
... story generation, but also essential aspects for any text generation task out- putting a long ...based text generation, one initial attempt for tackling these issues is to combine RNN ... See full document
8
Semi-supervised adversarial variational autoencoder
... [9], text classification [10], sentence generation [11], speech synthesis and recognition [12] [13] [14], spatio-temporal solar irradiance forecasting [15] and in geoscience for data assimilation ... See full document
17
Modeling Event Background for If Then Commonsense Reasoning Using Context aware Variational Autoencoder
... In dialogue generation, Zhao et al. (2017) adapts VAE with encoder-decoder framework to model the latent semantic distribution of answers, which can increase the diversity of generations. For the task of machine ... See full document
10
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
... Recurrent Variational Autoencoder has been widely used for language modeling and text generation ...tional Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text ... See full document
11
A Hybrid Convolutional Variational Autoencoder for Text Generation
... coder so that the decoder’s receptive field is lim- ited. They demonstrate that this allows for a better control of KL and reconstruction terms. Hu et al. (2017) build a VAE for text generation and de- sign ... See full document
11
A Stable Variational Autoencoder for Text Modelling
... We evaluate our model against several strong baselines which apply VAE for text mod- elling (Bowman et al., 2016; Yang et al., 2017; Xu and Durrett, 2018). We conducted experi- ments based on two public benchmark ... See full document
6
Better Exploiting Latent Variables in Text Modeling
... Bowman et al. (2016) first proposed an LSTM- VAE model for text. They observed the posterior- collapse problem in which the approximate pos- terior collapses to the prior, and the model ig- nores the latent ... See full document
6
Comparative Study of GAN and VAE
... Generative Adversarial Network[1] has shown a very good results in many tasks to generate images, music, text etc.It takes a game theoretic approach unlike other conventional generative models, which learns to ... See full document
5
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models
... undesirable minima in the energy landscape of what would otherwise resemble a more traditional deterministic autoencoder (AE) (Bengio, 2009). This is true even in certain situations where it provably does not ... See full document
42
Diversity aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
... Early studies memorized event sequences ex- tracted from a corpus and inevitably suffered from low generalization capability and a scala- bility problem. A promising approach to mod- eling wide-coverage knowledge is to ... See full document
10
Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
... an autoencoder, which inputs and recon- structs the summaries, to obtain a better repre- sentation to generate the ...of autoencoder by minimizing the dis- tance between two ... See full document
7
A K Competitive Autoencoder for Aggression Detection in Social Media Text
... each text was to be classified either as aggressive or ...media text is classified into three categories, hate speech, aggressive text and ... See full document
10
A Brief Introduction to Z39.50 Protocol
... Record Syntax (GRS)-GRS is a general-purpose record syntax that allows for the retrieval of different types of structured records, whether they are full text or mixed ...the syntax for the ... See full document
41
The Unified Annotation of Syntax and Discourse in the Copenhagen Dependency Treebanks
... where virtually all mainstream theories of syntax opt for one of the two other analyses. Perhaps current theories of discourse structure perceive discourse structure as a semantic rather than syn- tactic ... See full document
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