Synthetic Text Generation Task

A Synthetic Text Generation Task is a text generation task (of textual content that is an AI task) aims to produce text that resembles human writing, tailored to meet specific criteria or inputs.

Context:
- It can aim to bridge the gap between human and machine-generated text, offering solutions that can adapt to context, style, and specific content requirements.
- It can support Complex NLP Tasks (such as conversation simulation and content creation).
- It can support the development of more intelligent and responsive AI applications, from Chatbots to personalized content generators.
- It can support NLP Data Augmentation (by helping to expand datasets where textual data may be limited or costly to obtain manually).
- ...
Example(s):
- Realistic Dialog Generation for training conversational agents.
- Contract Synthetic Text Generation Task.
- Creating synthetic reviews, articles, or social media posts.
- ...
Counter-Example(s):
- Manual Synthetic Text Generation.
See: Synthetic Text Generation System, Synthetic Text Generation Algorithm, Natural Language Processing, Data Augmentation.

References

(Halterman, 2023) ⇒ Andrew Halterman. (2023). “Synthetically Generated Text for Supervised Text Analysis.” In: arXiv preprint arXiv:2303.16028. [1](https://arxiv.org/abs/2303.16028)
- NOTE:
  - It highlights a method to enhance the quality of synthetically generated text, emphasizing the balance between the benefits of synthetic text and the tradeoffs involved in its generation.
  - It provides insights into optimizing synthetic text for supervised text analysis.
  - It proposes using synthetic text generation with language models to lower costs of supervised text analysis like labeling and sharing text.

(He et al., 2022) ⇒ Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, and Mohammad Norouzi. (2022). “Generate, Annotate, and Learn: NLP with Synthetic Text." Transactions of the Association for Computational Linguistics, 10. DOI:10.48550/arXiv.2106.06168
- NOTE: It explores the role of diversity in synthetic text for natural language processing, demonstrating that simple unconditional generation with random seeds can provide sufficient diversity for crafting diverse language models.

(Srivastava & Singh, 2021) ⇒ Vivek Srivastava, and Mayank Singh. (2021). “Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text.” In: arXiv preprint arXiv:2108.01861. [2](https://arxiv.org/abs/2108.01861)
- NOTE: It explores the quality evaluation of synthetically generated code-mixed Hinglish text, identifying factors that influence text quality. This research contributes to the development of high-quality code-mixed text generation models, with a focus on low-resource languages.

(Munir et al., 2021) ⇒ Shaoor Munir, Brishna Batool, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar. (2021). “Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models." In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1811-1822. [3](https://www.aclweb.org/anthology/2021.eacl-main.159/)
- NOTE: It addresses the challenge of attributing authorship to synthetically generated text by language models, proposing a method for identifying the source language model of a given piece of synthetic text.

(Jaderberg et al., 2014) ⇒ Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. (2014). “Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition." arXiv preprint arXiv:1406.2227. [4](https://arxiv.org/abs/1406.2227)
- NOTE: It pioneers the use of synthetic data and artificial neural networks for natural scene text recognition, detailing the generation of large-scale synthetic datasets for training and validating text recognition models.