# TAG : Type Auxiliary Guiding for Code Comment Generation

arXiv: https://arxiv.org/abs/2005.02835 · HTML: https://mlsanity.com/paper/2005.02835/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

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

- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [Impact of Evaluation Methodologies on Code Summarization](https://mlsanity.com/paper/2108.09619/) (2021)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Learning to Generate Code Comments from Class Hierarchies](https://mlsanity.com/paper/2103.13426/) (2021)
- [Jointly Learning to Repair Code and Generate Commit Message](https://mlsanity.com/paper/2109.12296/) (2021)
- [Implant Global and Local Hierarchy Information to Sequence based Code Representation Models](https://mlsanity.com/paper/2303.07826/) (2023)
- [Selective Shot Learning for Code Explanation](https://mlsanity.com/paper/2412.12852/) (2024)

## Abstract (arXiv)

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information. In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node. Specifically, our framework is featured with a Type-associated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code. We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework. Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.