In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.

中文翻译：

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用于和谐人机交互的条件文本生成

近年来，随着深度学习的发展，文本生成技术发生了翻天覆地的变化，为人类提供了餐厅预订、日常交流等多种服务。自动生成的文本越来越流畅，因此研究人员开始考虑更多拟人化的文本生成技术，即条件文本生成，包括情感文本生成、个性化文本生成等。条件文本生成（CTG）因此成为研究热点。作为一个有前途的研究领域，我们发现人们对它的探索给予了很多关注。因此，我们旨在全面回顾CTG的新研究趋势。我们首先总结了几个关键技术，并说明了神经文本生成领域的技术演进路线，基于CTG的概念模型。我们进一步对现有的 CTG 领域进行了调查，并提出了几种 CTG 的通用学习模型。最后，我们讨论了 CTG 的开放性问题和有前景的研究方向。