Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.
( Image credit: Adversarial Ranking for Language Generation )
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In this work, we have introduced Gaussian Smoothen Semantic Features (GSSF) for Better Semantic Selection for Indian regional language-based image captioning and introduced a procedure where we used the existing translation and English crowd-sourced sentences for training.
While able to be trained in a fully self-supervised fashion, our model can be further fine-tuned with a little amount of human preference annotation to better imitate human judgment.
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns.
In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities.
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples.
Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input.
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.