可控文本生成技术研究综述

王舰,孙宇清

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中文信息学报 ›› 2024, Vol. 38 ›› Issue (10) : 1-23.
综述

可控文本生成技术研究综述

  • 王舰,孙宇清
作者信息 +

Survey on Controllable Text Generation

  • WANG Jian, SUN Yuqing
Author information +
History +

摘要

可控文本生成任务是指生成符合语法规则和语义需求,且满足给定约束的自然语言文本,具有重要应用价值。如何将约束嵌入到隐空间,从而有效控制离散的词汇生成过程是十分困难的,特别是在复杂应用场景中: 不仅需要控制文本内容,还要求生成的长文本形式多样、语言灵活以及逻辑合理等,这使得可控文本生成任务更具挑战性且难以评估。近年来,数据驱动的神经方法得到了广泛应用,特别是大规模预训练语言模型大幅度提升了生成文本质量。该文综述这些生成方法中的代表性技术架构和模型,讨论文本生成领域定性和定量评价指标,以及相关数据集;针对可控文本生成任务的文本多样性和句子间语义一致性等高层次需求,重点讨论相关技术前沿进展,分析其理论依据和技术优势;最后总结可控文本生成任务仍然面临的挑战和未来发展方向。

Abstract

The controllable text generation task is to generate a natural language text that satisfies grammatical rules and semantic requirements under constraints in practical scenarios. It is difficult to embed the constraints into latent space to control the text generation process in an explicit way. Especially in the complex scenarios, the generated texts should be linguistically diverse and semantic consistency in addition to satisfying the constraints. In recent years, the data-driven controllable text generation methods have become the mainstream, especially the use of large-scale pre-trained language models and the generative adversarial networks significantly improve the quality of generated text. We summarize the representative technical architecture and models, the qualitative and quantitative metrics as well as the task-related datasets. Focusing on the challenging requirements such as the linguistic diversity and semantic relevance in long texts, we survey the theories and techniques of the related methods, as well as discuss the advantages and shortcomings. At last, we summarize the remaining challenges and present some promising research directions for the controllable text generation and evaluation.

关键词

可控文本生成 / 文本评估 / 文本多样性 / 长文本生成

Key words

controlled text generation / text evaluation / text diversity / long text generation

引用本文

导出引用
王舰,孙宇清. 可控文本生成技术研究综述. 中文信息学报. 2024, 38(10): 1-23
WANG Jian, SUN Yuqing. Survey on Controllable Text Generation. Journal of Chinese Information Processing. 2024, 38(10): 1-23

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基金

国家自然科学基金(62376138);山东省自然科学基金创新发展联合基金(ZR2022LZH007)
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