We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.

我们解决了在社区问答（CQA）网站中找到与输入查询问题在语义上等效或相关的类似历史问题的问题。此任务的主要挑战之一是问题通常太长，并且除了问题的主要目标外，还经常包含外围信息。为了解决这个问题，我们提出了一个终端到终端^ h ierarchical Ç ompare一个ggregate（HCA）模式，可以解决这个问题，而无需使用任何特定任务的功能。我们首先将问题分解为句子，然后使用拟议的W ord- L evel- C ompare- A比较两个问题的每个句子对ggregate模式叫WLCA模型，然后将比较结果与提议的聚集小号entence-大号evel- Ç ompare-一个ggregate模型做出最终决定。为了处理训练数据不足的问题，我们提出了一种顺序转移学习方法，以在大型复述检测数据集上对WLCA模型进行预训练。我们对两个版本的Semeval基准数据集和特定于域的AskUbuntu数据集进行的实验表明，我们的模型优于最新模型。