HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning

Researchers introduced HintMiner, an automatic question-hints mining tool that extracts hints from web Q&A posts using MiningNet, a transformer-based neural encoder-decoder model with copying mechanisms trained via a self-supervised objective. Evaluated on 60,000 Stack Overflow questions, HintMiner achieved an average BLEU score of 36.17% and an average ROUGE-2 score of 36.29%, according to the arXiv paper 2609.16060v1. The tool and experimental data are publicly available.

arXiv:2609.16060v1 Announce Type: new Abstract: Users often need ask questions and seek answers online. The Question - Answering QA forums such as Stack Overflow cannot always respond to the questions timely and properly. In this paper, we propose HintMiner, a novel automatic question hints mining tool for users to help them find answers. HintMiner leverages the machine comprehension and sequence generation techniques to automatically generate hints for users' questions. It firstly retrieve many web Q\&A posts and then extract some hints from the posts using MiningNet that is built via a language model. Using the huge amount of online Q\&A posts, we design a self-supervised objective to train the MiningNet that is a neural encoder-decoder model based on the transformer and copying mechanisms. We have evaluated HintMiner on 60,000 Stack Overflow questions. The experiment results show that the proposed approach is effective. For example, HintMiner achieves an average BLEU score of 36.17\% and an average ROUGE-2 score of 36.29\%. Our tool and experimental data are publicly available.