## 1 Introduction  
With the development of artificial intelligence technology, answer selection has become a hot and urgent research topic in natural language processing (NLP) because it is the foundational condition for intelligent voice assistants and intelligent medical service robots to understand the question and realize intelligent answering service. Answer selection is designed to select the correct answer based on a given question and corresponding candidate answers and reflects the matching relationship between question and candidate answers by extracting deep features of the question-answer (QA) pair. It is the basis for developing more efficient intelligent Q&A systems, such as medical voice Q&A service robots, which are of significant interest in the medical field [1, 2, 3, 4]. Answer selection is of abundant importance in intelligent question answering (Q&A) tasks and draws increasing attention from researchers. A broad range of prior research studies have been proposed in recent years. Traditional answer selection models mostly use lexical or syntactic analysis and manual marking to select answers. It is difficult to capture the semantic connection between QA pairs. With the development of deep learning, some prominent performance frameworks have been introduced to answer selection tasks, among which are convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM) [5], etc. These neural networks can better understand the relationship between QA pairs. Most of the existing models of answer selection are generative models. The generative model learns a Q&A mapping to generate responses by maximizing P (a|q), where q is the input question and a is the answer. The most popular generative model is the sequence-to-sequence (Seq2 Seq) model [6], which consists of an encoder and a decoder. Seq2 Seq can achieve high coherence between questions and answers. Reference [7] first used a Bi-LSTM encoder to encode question and candidate answers, and then adopted feed-forward attention to encode a question sentence. A growing number of researchers connect deep learning models with other advanced technologies to improve the ability of answer selection model. Good results have been achieved by utilizing these neural networks. Although they pay much attention to the semantic understanding of natural language and global information of context, answer selection models still face the following challenges due to the complexity of natural language. Firstly, questions and answers are semantically connected, thus, it is not enough to just utilize the similarity as an indicator. Secondly, some noise and information not related to the question can interfere with the process of choosing the correct answer. Therefore, we need to design a refined answer selection model to address these problems. This paper proposes a novel refined answer selection method with an attentive bidirectional long short-term memory network and a self-attention mechanism to address these challenges based on the observations above. Specifically, it adopts self-attention to obtain global information and designs an attentive Bi-LSTM network to gain a deeper understanding of the semantic information of the dataset. The experimental results reveal that the proposed method could achieve better performance on intelligent answer selection tasks. It is of great importance for a medical service robot to realize an intelligent question answering service. The main contributions of this article are summarized as follows:  
1. A refined answer selection method is proposed for a medical service robot to realize an intelligent question-answering service. The required knowledge-based text is constructed as background information to match the question and answer. 2. Self-attention is adopted to extract global features of the input data before passing the data to the circulating layer to solve the long-distance dependent learning.