In the past decade, dialogue systems have become an attractive topic and they can be classified into open-domain dialogue systems and task-oriented dialogue systems. One general approach to dialogue system design is to treat it as a retrieval problem by learning the relevance matching score between user queries and system responses. Inspired by the recent advances in deep learning, building an end-to-end dialogue system has been a popular approach for its flexibility and extendibility. For example, encoder-decoder models based on recurrent neural networks (RNNs) directly maximize the likelihood of the desired responses when previous dialogue history data are available. However, one of the major drawbacks of those systems is that multiple training corpora are required and generic responses such as "I do not know" are likely to be generated. These drawbacks limit the generalization ability, especially for a task-oriented system in which knowledge from multiple domains is needed to understand users' underlying intents.

Compared to end-to-end approach, designing a task-oriented dialogue system as modularized pipeline is feasible. And each essential component is trained individually, including 1) Natural Language Understanding (NLU), to specify task domain and user intent and extract slot-value pairs; 2) Dialogue Manager (DM), to keep tracking the dialogue state and guide users to achieve a desired goal; 3) Natural Language Generation (NLG), to generate responses. One of the challenges for a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains. If earlier components make mistakes in slot value extraction and errors are accumulated, the entire system's functionality will be severely impaired.

To address the complex dialogue state transition problem, we adopt the architecture of modularized pipeline and propose a multi-domain KB-BOT (MDKB-BOT), which leverages both rule extraction and neural networks. We run the evaluation experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign and experimental results show that MDKB-BOT can robustly fulfill the frequent changes of user intent among three domains (flight, train and hotel) and achieve competitive scores based on human evaluation metrics.

As mentioned before, have been a lot of research efforts in applying deep learning to task-oriented dialogue systems. One of the most effective approaches is to build a modularized pipeline system by connecting NLU, DM and NLG together. Traditional approach to NLU is to model domain classification/intent detection as sentence classification while treating slot value pairs extraction as a sequence labeling task. A desirable NLU system should not be sensitive to intent error and slot error, especially for slot filling. For example, Xu and Sarikaya [1] applied a RNN to perform contextual domain classification and used a triangular conditional random field (CRF) based on a convolutional neural network for intent detection and slot filling. Jaech, Heck and Ostendorf [2] applied multi-task learning to achieve the goal to leverage knowledge of source domains with rich data to improve the performance on the target domain with little data. Bapna et al. [3] explored the role of context information played in NLU via injecting previous dialogues into a RNN based encoder and a memory network.

On the other hand, many attempts have been made to improve the architecture of DM. Recent research indicates that reinforcement learning (DL) holds promise for planning dialogue policy based on the current dialogue state. Williams, Asadi, and Zweig [4] proposed a model called Hybrid Code Networks (HCNs), which is a mixture of supervised learning and reinforcement learning [4]. HCNs select a dialogue action every step by optimizing the reward for completing a task with policy gradient [5]. Faced with the sparse nature of the reward signal in RL, Peng et al. [6] designed an end-to-end framework for hierarchical RL where a MANAGER is used to choose current goal (like a specific domain task) and a WORKER is used to take actions and help users finish the current subtask. Inspired by recent advances in RL, Mrkšíc et al. [7] introduced a belief tracker that can overcome the drawback of requiring a large amount of hand-crafted lexicons to capture some of the linguistic variation in users' language. Their Neural Belief Tracking (NBT) models can reason over pre-trained word embeddings of system output, user utterance and candidate pairs in databases.

As for NLG, most of the current work applied information retrieval technique to a large query-response database, or used template-based methods with a set of rules to map frames to natural language or generation models. Dušek and Jurčíček [8] encoded frames based on the syntax tree and used seq2seq model for generation.

The proposed framework is illustrated in Figure 1, which includes NLU, DM and NLG. The implementation of these components is described from Section 3.1 to Section 3.3.

In the Chinese Human-Computer Dialogue Technology Evaluation Campaign, a task-oriented dialogue system is developed to help users book flights, trains and hotels.

Table 3 shows the results of slot filling. We compare the performance of LSTM-CRF and BLSTM-CRF with unigram and unigram plus bigram separately. As illustrated, accuracy can increase by 1.7% when considering word sequence order with BLSTM. Using bigram feature can be of help for LSTM-CRF, though it is worse for BLSTM as the average length of bigram sequence is short. One possible way is to use character embedding instead of word embedding.

Table 4 illustrates the performance of our system according to the evaluation metrics mentioned before. Most of the metrics are annotated manually except for the average dialogue turn. our system obtained the best score on user satisfaction, naturalness and boot ability metrics due to the reasonable dialogue intent transition template we predefined. But this leads to a decline in task performance, especially when the user intent is not identified or the important slot value is not extracted correctly.

In this paper, we proposed a simple but practical framework for multi-domain task-oriented dialogue system. Our model leverages both neural network and rule-based strategy to handle the domain transition problem. It achieves competitive results on the Chinese Human-Computer Dialogue Technology Evaluation Campaign, especially for user-friendliness and utterance guidance metrics. For future work, we are going to apply end-to-end neural networks to NLG based on the information extracted and maintained in NLU and DM to improve the system performance.