{"list":"other","total":68,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"1508.05326","title":"A large annotated corpus for learning natural language inference","created":"2015-08-21","cited_by_count":1847},{"id":"1511.07122","title":"Multi-Scale Context Aggregation by Dilated Convolutions","created":"2015-11-23","cited_by_count":1133},{"id":"1510.03055","title":"A Diversity-Promoting Objective Function for Neural Conversation Models","created":"2015-10-11","cited_by_count":1077},{"id":"1511.06349","title":"Generating Sentences from a Continuous Space","created":"2015-11-19","cited_by_count":612},{"id":"1308.0850","title":"Generating Sequences With Recurrent Neural Networks","created":"2013-08-04","cited_by_count":554},{"id":"1406.5298","title":"Semi-Supervised Learning with Deep Generative Models","created":"2014-06-20","cited_by_count":496},{"id":"1802.01886","title":"Texygen: A Benchmarking Platform for Text Generation Models","created":"2018-02-06","cited_by_count":491},{"id":"1506.02557","title":"Variational Dropout and the Local Reparameterization Trick","created":"2015-06-08","cited_by_count":490},{"id":"1509.00519","title":"Importance Weighted Autoencoders","created":"2015-09-01","cited_by_count":456},{"id":"1509.00685","title":"A Neural Attention Model for Abstractive Sentence Summarization","created":"2015-09-02","cited_by_count":396},{"id":"1609.05473","title":"SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient","created":"2016-09-18","cited_by_count":376},{"id":"1511.05644","title":"Adversarial Autoencoders","created":"2015-11-18","cited_by_count":364},{"id":"1703.00955","title":"Toward Controlled Generation of Text","created":"2017-03-02","cited_by_count":272},{"id":"1705.09655","title":"Style Transfer from Non-Parallel Text by Cross-Alignment","created":"2017-05-26","cited_by_count":248},{"id":"1707.08052","title":"Challenges in Data-to-Document Generation","created":"2017-07-25","cited_by_count":226},{"id":"1703.10960","title":"Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders","created":"2017-03-31","cited_by_count":222},{"id":"1507.04808","title":"Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models","created":"2015-07-17","cited_by_count":207},{"id":"1903.10145","title":"Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing","created":"2019-03-25","cited_by_count":191},{"id":"1802.04942","title":"Isolating Sources of Disentanglement in Variational Autoencoders","created":"2018-02-14","cited_by_count":167},{"id":"1711.06861","title":"Style Transfer in Text: Exploration and Evaluation","created":"2017-11-18","cited_by_count":146},{"id":"1706.02262","title":"InfoVAE: Information Maximizing Variational Autoencoders","created":"2017-06-07","cited_by_count":124},{"id":"2004.04092","title":"Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space","created":"2020-04-05","cited_by_count":114},{"id":"1709.08624","title":"Long Text Generation via Adversarial Training with Leaked Information","created":"2017-09-24","cited_by_count":100},{"id":"1702.08139","title":"Improved Variational Autoencoders for Text Modeling using Dilated Convolutions","created":"2017-02-27","cited_by_count":81},{"id":"1412.7449","title":"Grammar as a Foreign Language","created":"2014-12-23","cited_by_count":70},{"id":"1908.06605","title":"Long and Diverse Text Generation with Planning-based Hierarchical Variational Model","created":"2019-08-19","cited_by_count":62},{"id":"1808.10122","title":"Learning Neural Templates for Text Generation","created":"2018-08-30","cited_by_count":61},{"id":"1907.05789","title":"Generating Sentences from Disentangled Syntactic and Semantic Spaces","created":"2019-07-06","cited_by_count":59},{"id":"1706.03850","title":"Adversarial Feature Matching for Text Generation","created":"2017-06-12","cited_by_count":58},{"id":"2006.00693","title":"Improving Disentangled Text Representation Learning with Information-Theoretic Guidance","created":"2020-06-01","cited_by_count":52},{"id":"1811.01135","title":"Content preserving text generation with attribute controls","created":"2018-11-03","cited_by_count":47},{"id":"1903.07137","title":"Topic-Guided Variational Autoencoders for Text Generation","created":"2019-03-17","cited_by_count":44},{"id":"2101.00828","title":"Transformer-based Conditional Variational Autoencoder for Controllable Story Generation","created":"2021-01-04","cited_by_count":44},{"id":"1611.09630","title":"Improving Variational Auto-Encoders using Householder Flow","created":"2016-11-29","cited_by_count":42},{"id":"1607.00970","title":"Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation","created":"2016-07-04","cited_by_count":35},{"id":"1905.12926","title":"Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation","created":"2019-05-30","cited_by_count":34},{"id":"1702.02390","title":"A Hybrid Convolutional Variational Autoencoder for Text Generation","created":"2017-02-08","cited_by_count":33},{"id":"1909.00868","title":"A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text","created":"2019-09-02","cited_by_count":32},{"id":"1610.02287","title":"The Generalized Reparameterization Gradient","created":"2016-10-07","cited_by_count":30},{"id":"1911.03882","title":"Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders","created":"2019-11-10","cited_by_count":27},{"id":"1908.11527","title":"Implicit Deep Latent Variable Models for Text Generation","created":"2019-08-30","cited_by_count":26},{"id":"2010.02983","title":"Plug and Play Autoencoders for Conditional Text Generation","created":"2020-10-06","cited_by_count":26},{"id":"1705.09207","title":"Learning Structured Text Representations","created":"2017-05-25","cited_by_count":24},{"id":"1610.05683","title":"Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms","created":"2016-10-18","cited_by_count":22},{"id":"2207.06130","title":"Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation","created":"2022-07-13","cited_by_count":20},{"id":"2002.01127","title":"Variational Template Machine for Data-to-Text Generation","created":"2020-02-04","cited_by_count":19},{"id":"2109.07169","title":"Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders","created":"2021-09-15","cited_by_count":19},{"id":"2108.02446","title":"Finetuning Pretrained Transformers into Variational Autoencoders","created":"2021-08-05","cited_by_count":14},{"id":"2007.15780","title":"Neural Language Generation: Formulation, Methods, and Evaluation","created":"2020-07-31","cited_by_count":13},{"id":"1809.03015","title":"Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?","created":"2018-09-09","cited_by_count":12},{"id":"1712.09783","title":"Topic Compositional Neural Language Model","created":"2017-12-28","cited_by_count":11},{"id":"1812.06834","title":"A Tutorial on Deep Latent Variable Models of Natural Language","created":"2018-12-17","cited_by_count":10},{"id":"2205.05862","title":"AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling","created":"2022-05-12","cited_by_count":10},{"id":"2010.12055","title":"A Discrete Variational Recurrent Topic Model without the Reparametrization Trick","created":"2020-10-22","cited_by_count":9},{"id":"1903.10842","title":"Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder","created":"2019-03-26","cited_by_count":6},{"id":"1811.00135","title":"Dirichlet Variational Autoencoder for Text Modeling","created":"2018-10-31","cited_by_count":5},{"id":"2210.03496","title":"PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation","created":"2022-10-07","cited_by_count":4},{"id":"1911.03828","title":"Stylized Text Generation Using Wasserstein Autoencoders with a Mixture of Gaussian Prior","created":"2019-11-10","cited_by_count":2},{"id":"2110.07002","title":"Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation","created":"2021-10-13","cited_by_count":1}],"next_cursor":null}