Building dialogue systems plays an important role in modern life. amongst them, task-oriented dialogue for resolving problems in actual life is most worth exploring. Motivated by the development of end-to-end approaches, a task-oriented dialogue model based on bidirectional LSTM and self-attention mechanism is proposed. It not only makes good use of context and effectively solves the long-term dependency, but also identifies the relationship between sentences, optimizes feature vectors, and has good parallelism. In our method, the dialogue state tracker(DST) is firstly improved. Our dialogue state tracker can identify the multiple slot key-value pairs involved in the utterance without manual label. In addition, we apply the data augmentation of merging machine translation and bilingual dictionary to create more diversified data sets. Finally, in the experimental part, the enhanced data and the results of DST are fed into the proposed B&Anet (bidirectional long and short memory network and self-attention mechanism network) model. The evaluation results on DSTC2 (dialogue state tracking chanllenge2) show that our method achieves competitive performance.

建立对话系统在现代生活中起着重要作用。其中，最有必要探索解决任务中的面向任务的对话。基于端到端方法的发展，提出了一种基于双向LSTM和自我注意机制的面向任务的对话模型。它不仅充分利用了上下文，有效地解决了长期依赖，而且识别句子之间的关系，优化了特征向量，并具有良好的并行性。在我们的方法中，首先改进了对话状态跟踪器（DST）。我们的对话状态跟踪器无需使用人工标签即可识别发声中涉及的多个广告位键值对。另外，我们应用合并机器翻译和双语词典的数据增强来创建更加多样化的数据集。最后，在实验部分，将增强的数据和DST的结果输入到所提出的B＆Anet（双向长短存储网络和自我关注机制网络）模型中。对DSTC2的评估结果（对话状态跟踪chanllenge2）表明，我们的方法具有竞争优势。