It has been an open research challenge for developing an end-to-end multi-domain task-oriented dialogue system, in which a human can converse with the dialogue agent to complete tasks in more than one domain. First, tracking belief states of multi-domain dialogues is difficult as the dialogue agent must obtain the complete belief states from all relevant domains, each of which can have shared slots common among domains as well as unique slots specifically for the domain only. Second, the dialogue agent must also process various types of information, including contextual information from dialogue context, decoded dialogue states of current dialogue turn, and queried results from a knowledge base, to semantically shape context-aware and task-specific responses to human. To address these challenges, we propose an end-to-end neural architecture for task-oriented dialogues in multiple domains. We propose a novel Multi-level Neural Belief Tracker which tracks the dialogue belief states by learning signals at both slot and domain level independently. The representations are combined in a Late Fusion approach to form joint feature vectors of (domain, slot) pairs. Following recent work in end-to-end dialogue systems, we incorporate the belief tracker with generation components to address end-to-end dialogue tasks. We achieve state-of-the-art performance on the MultiWOZ2.1 benchmark with 50.91% joint goal accuracy and competitive measures in task-completion and response generation. In a task-oriented dialogue system, the Dialogue State Tracking (DST) module is responsible for updating dialogue states (essentially, what the user wants) at each dialogue turn. The DST supports the dialogue agent to steer the conversation towards task completion. As defined by Henderson et al. (2014a) , a dialogue belief state consists of inform slots -information to query a given knowledge base or database (DB), and request slots -information to be returned to the users. Task-oriented dialogues can be categorized as either single-domain or multi-domain dialogues. In single-domain dialogues, humans converse with the dialogue agent to complete tasks of one domain. In contrast, in multi-domain dialogues, the tasks of interest can come from different domains. A dialogue state in a multi-domain dialogue should include all inform and request slots of corresponding domains up to the current turn. Examples of a single-domain dialogue and a multi-domain dialogue with annotated states after each turn can be seen in Figure 1 . Despite there being several efforts in developing task-oriented dialogue systems in a single domain (Wen et al., 2016a; Lei et al., 2018) , there have been limited contributions for multi-domain task-oriented dialogues. Developing end-to-end systems for multi-domain dialogues faces several challenges: (1) Belief states in multi-domain dialogues are usually larger and more complex than in single-domain, because of the diverse information from multiple domains. Each domain can have shared slots that are common among domains or unique slots that are not shared with any. (2) In an end-to-end system, the dialogue agent must incorporate information from source sequences, e.g. dialogue context and human utterances, as well as tracked belief states and extracted information from knowledge base, to semantically shape a relevant response with accurate information for task completion. Directly applying methods for single-domain dialogues to multi-domain dialogues is not straightforward because the belief states extend across multiple domains. A possible solution is to process a multi-domain dialogue for N D times for N D domains, each time obtaining a belief state of one domain. However, this approach does not allow learning co-references in dialogues whereby users can switch from one domain to another turn by turn. We propose an end-to-end dialogue system approach which explicitly track the dialogue states in multiple domains altogether. Specifically, (1) we propose Multi-level Neural Belief Tracker to process contextual information for both slot-level and domain-level signals independently. The two levels are subsequently combined to learn multi-domain dialogue states. Our dialogue state tracker enables shared learning of slots common among domains as well as learning of unique slots in each domain. (2) we utilize multi-head attention layers (Vaswani et al., 2017) to comprehensively process various types of information: dialogue context, user utterances, belief states of both inform and request slots, and DB query results. The multi-head structure allows the model to independently attend to the features over multiple representation sub-spaces; and (3) we combine all components to create a dialogue system from state tracking to response generation. The system can be jointly learned in an end-to-end manner. Our end-to-end dialogue system utilizes supervision signals of dialogue states and output responses without using system action annotation. To comprehensively validate our method, we compare our models with baselines in end-to-end, DST, and context-to-text generation settings. We achieve the state-of-the-art performance in DST, task-completion, and response generation in the MultiWOZ2.1 corpus Eric et al., 2019 ) as compared to other baselines in similar settings. In context-to-text generation setting that allows supervision of dialogue acts, our models can achieve competitive measures of Inform and BLEU metric. In this work, we proposed an end-to-end dialogue system with a novel Multi-level Neural Belief Tracker. Our DST module can track complex belief states of multiple domains and output more accurate dialogue states. The DST is combined with attention-based generation module to generate dialogue responses. Evaluated on the large-scale multi-domain dialogue benchmark MultiWOZ2.1, our models achieve the state-of-the-art performance in DST and competitive measures in taskcompletion and response generation. Figure 3 : Example dialogue with the input system response St−1 and current user utterance Ut, and the output belief state BSt and system response St. Compared with TSCP (Row 3), our dialogue state and response (Last Row) are more correct and closer to the ground truth (Row 2). Visualization of attention to the user utterance sequence at slot-level (lower right) and domain-level (upper right) is also included. More red denotes higher attention score between domain or slot representation and token representation. Best viewed in color.
We proposed an end-to-end dialogue system with a novel multi-level dialogue state tracker and achieved consistent performance on MultiWOZ2.1 in state tracking, task completion, and response generation performance.
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