# Dopamin: Transformer-based Comment Classifiers through Domain Post-Training and Multi-level Layer Aggregation

arXiv: https://arxiv.org/abs/2408.04663 · HTML: https://mlsanity.com/paper/2408.04663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://mlsanity.com/paper/2305.06156/) (2023)
- [Improving BERT Performance for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2010.11731/) (2020)
- [Better Language Models of Code through Self-Improvement](https://mlsanity.com/paper/2304.01228/) (2023)
- [Automatic Identification of Self-Admitted Technical Debt from Four Different Sources](https://mlsanity.com/paper/2202.02387/) (2022)
- [How to Identify Class Comment Types? A Multi-language Approach for Class Comment Classification](https://mlsanity.com/paper/2107.04521/) (2021)
- [STACC: Code Comment Classification using SentenceTransformers](https://mlsanity.com/paper/2302.13149/) (2023)

## Abstract (arXiv)

Code comments provide important information for understanding the source code. They can help developers understand the overall purpose of a function or class, as well as identify bugs and technical debt. However, an overabundance of comments is meaningless and counterproductive. As a result, it is critical to automatically filter out these comments for specific purposes. In this paper, we present Dopamin, a Transformer-based tool for dealing with this issue. Our model excels not only in presenting knowledge sharing of common categories across multiple languages, but also in achieving robust performance in comment classification by improving comment representation. As a result, it outperforms the STACC baseline by 3% on the NLBSE'24 Tool Competition dataset in terms of average F1-score, while maintaining a comparable inference time for practical use. The source code is publicity available at https://github.com/FSoft-AI4Code/Dopamin.