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Found 27 papers, 13 papers with code
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Data augmentation is an effective approach to tackle over-fitting.
Dense-to-Sparse Gate for Mixture-of-Experts
However, we found that the current approach of jointly training experts and the sparse gate introduces a negative impact on model accuracy, diminishing the efficiency of expensive large-scale model training.
NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Local Additivity Based Data Augmentation for Semi-supervised NER
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data.
Progressive Generation of Long Text with Pretrained Language Models
However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e. g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus.
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.
Let's Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms
Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising.
Data-to-Text Generation with Style Imitation
That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.
Connecting the Dots Between MLE and RL for Sequence Prediction
Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.
Connecting the Dots Between MLE and RL for Sequence Generation
We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters.
Differentiable Expected BLEU for Text Generation
Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable advances.
Unsupervised Text Style Transfer using Language Models as Discriminators
Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain.
On Unifying Deep Generative Models
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively.
Toward Controlled Generation of Text
Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain.
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015).
Ranked #3 on
Text Generation
on Yahoo Questions
Reference-Aware Language Models
We propose a general class of language models that treat reference as an explicit stochastic latent variable.
Ranked #1 on
Recipe Generation
on allrecipes.com
Neural Machine Translation with Recurrent Attention Modeling
Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future.
Stacked Attention Networks for Image Question Answering
Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively.
Ranked #5 on
Visual Question Answering
on VQA v1 test-std
Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters.
Ranked #56 on
Image Classification
on MNIST
A la Carte - Learning Fast Kernels
Kernel methods have great promise for learning rich statistical representations of large modern datasets.
