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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Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field.
Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion.
Language-capable interactive robots participating in dialogues with human interlocutors must be able to naturally and efficiently communicate about the entities in their environment.
Recent studies showed that a considerable part of the knowledge of neural network Language Models (LM) can be transferred to traditional n-grams by using neural text generation based data augmentation.
CoreGen first learns contextualized code representation which exploits the contextual information behind code commit sequences.
Advanced machine learning and natural language techniques enable attackers to launch sophisticated and targeted social engineering-based attacks.
We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.