While large language models (LMs) have shown remarkable capabilities acr...
With the increasing data volume, there is a trend of using large-scale
p...
Real-world text applications often involve composing a wide range of tex...
Data augmentation is an effective approach to tackle over-fitting. Many
...
Mixture-of-experts (MoE) is becoming popular due to its success in impro...
Neural text generation models are typically trained by maximizing
log-li...
Named Entity Recognition (NER) is one of the first stages in deep langua...
Large-scale language models pretrained on massive corpora of text, such ...
This paper presents MixText, a semi-supervised learning method for text
...
Deep learning has revolutionized speech recognition, image recognition, ...
Controlled generation of text is of high practical use. Recent efforts h...
Sequence generation models such as recurrent networks can be trained wit...
We introduce Texar, an open-source toolkit aiming to support the broad s...
The broad set of deep generative models (DGMs) has achieved remarkable
a...
Binary classifiers are often employed as discriminators in GAN-based
uns...
Deep generative models have achieved impressive success in recent years....
Generic generation and manipulation of text is challenging and has limit...
Recent work on generative modeling of text has found that variational
au...
We propose a general class of language models that treat reference as an...
Knowing which words have been attended to in previous time steps while
g...
This paper presents stacked attention networks (SANs) that learn to answ...
The fully connected layers of a deep convolutional neural network typica...
Kernel methods have great promise for learning rich statistical
represen...