AI Text Generation: Past, Present, and What's Next
AI that writes: past, present, future
In a sweeping survey, Zhang, Guo, Wang, Liang, Hao, and Yu trace how text generation has evolved - from simple templates to neural networks that can draft emails, stories, and more.
- From fluency to personality: systems now aim to reflect tone, style, and user intent.
- From rules to learning: methods span templates, probabilistic models, encoder-decoder nets, and Transformers.
- Applications: chatbots, translation, summarization, data-to-text, and assistive writing.
- What's inside: a unified framework, widely used models, and a map of the state of the art.
Why it matters: better control, personalization, and evaluation will shape safer, more helpful human-machine communication.
Read the survey: http://arxiv.org/abs/1905.01984v1
Paper: http://arxiv.org/abs/1905.01984v1
Register: https://www.AiFeta.com
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