HintMiner：基于自监督学习的语言模型从问答网页帖子中自动挖掘问题提示
HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning
AI总结：
HintMiner利用自监督学习的语言模型从问答帖子中自动挖掘提示，通过Transformer和复制机制生成提示，在6万条Stack Overflow问题上取得BLEU 36.17%和ROUGE-2 36.29%的效果。
AI中文摘要：
用户经常需要在线提问并寻求答案。诸如Stack Overflow之类的问答（QA）论坛往往无法及时且恰当地回应所有问题。在本文中，我们提出了HintMiner，一种新颖的自动问题提示挖掘工具，旨在帮助用户找到答案。HintMiner利用机器理解和序列生成技术，自动为用户的问题生成提示。它首先检索大量网络问答帖子，然后使用通过语言模型构建的MiningNet从这些帖子中提取提示。利用海量的在线问答帖子，我们设计了一个自监督目标来训练MiningNet，这是一个基于Transformer和复制机制的神经编码器-解码器模型。我们在60,000个Stack Overflow问题上评估了HintMiner。实验结果表明，所提出的方法是有效的。例如，HintMiner实现了平均BLEU分数36.17%和平均ROUGE-2分数36.29%。我们的工具和实验数据公开可用。
英文摘要：
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.
