Task-oriented dialog systems which assist users to complete tasks like hotel reservation, are drawing great attentions among both research and industry. Compared to conventional pipelined system, recently emerging end-to-end trainable dialog systems are showing many favorable characteristics – because of the neural models that directly learn from chatlogs of human-to-human conversation employed, such systems hold the promise of low data preparation cost, flexible response generation and the ability to evolve with new data. In this work, we are going to explore the possibility to bring this end-to-end trainable framework to the hotel reservation chatbot application, where we encounter two new problems: 1. numerical slots-filling and 2. multi-turn dialog management. To the best of our knowledge, both of them can not be fully solved using currently available end-to-end frameworks. In this paper, we will focus on these two problems and propose possible workarounds which can lead to satisfactory results.
