# Context-aware Natural Language Generation with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1611.09900 · HTML: https://mlsanity.com/paper/1611.09900/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2016.

## Built on by (most cited first, 8 of 8)

- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [TextBox: A Unified, Modularized, and Extensible Framework for Text Generation](https://mlsanity.com/paper/2101.02046/) (2021)
- [Review-LLM: Harnessing Large Language Models for Personalized Review Generation](https://mlsanity.com/paper/2407.07487/) (2024)
- [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://mlsanity.com/paper/2209.13885/) (2022)
- [Document Automation Architectures and Technologies: A Survey](https://mlsanity.com/paper/2109.11603/) (2021)
- [Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews](https://mlsanity.com/paper/2307.03691/) (2023)
- [Document Automation Architectures: Updated Survey in Light of Large Language Models](https://mlsanity.com/paper/2308.09341/) (2023)
- [Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation](https://mlsanity.com/paper/2110.10358/) (2021)

## Abstract (arXiv)

This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic representation and then decode the semantic representation into text sequences with recurrent neural networks. During decoding, the context information are attended through a gating mechanism, addressing the problem of long-range dependency caused by lengthy sequences. We evaluate the effectiveness of the proposed approaches on user review data, in which rich contexts are available and two informative contexts, sentiments and products, are selected for evaluation. Experiments show that the fake reviews generated by our approaches are very natural. Results of fake review detection with human judges show that more than 50% of the fake reviews are misclassified as the real reviews, and more than 90% are misclassified by existing state-of-the-art fake review detection algorithm.