关键词: 迁移学习, 深度神经网络, FAQ问答

Abstract: Building an intelligent customer service system based on FAQ (frequent asked questions) is a technique commonly used in industry. Question answering systems based on FAQ offer numerous advantages including stability, reliability, and quality. However, given the practical limitations of scaling a manually annotated knowledge base, models often have limited recognition ability and can easily encounter bottlenecks. In order to address the problem of limited scale with FAQ datasets, this paper offers a solution at both the data level and the model level. At the data level, we use Baidu Knows to crawl relevant data and mine semantically equivalent questions, ensuring the relevance and consistency of the data. At the model level, we propose a deep neural network with transAT oriented transfer learning, which combines a transformer network and an attention network, and is suitable for semantic similarity calculations between sentence pairs. Experiments show that the proposed solution can significantly improve the impact of the model on FAQ datasets and to a certain extent resolve the issues with the limited scale of FAQ datasets.

Key words: transfer learning, deep neural network, FAQ (frequent asked questions) question-answering