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Found 6 papers, 5 papers with code
Few-shot Sequence Learning with Transformers
In the simplest setting, we append a token to an input sequence which represents the particular task to be undertaken, and show that the embedding of this token can be optimized on the fly given few labeled examples.
Zero-Shot Entity Linking by Reading Entity Descriptions
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
Content preserving text generation with attribute controls
We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic.
An efficient framework for learning sentence representations
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data.
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Modeling the structure of coherent texts is a key NLP problem.
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.
