Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020)

Abstract: Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager. The dialog memory manager dynamically expands the dialog memory turn by turn and keeps track of dialog history with an updating mechanism, which encourages the model to filter irrelevant dialog history and memorize important newly coming information. The KB memory manager shares the structural KB triples throughout the whole conversation, and dynamically extracts KB information with a memory pointer at each turn. Experimental results on three benchmark datasets demonstrate that DDMN significantly outperforms the strong baselines in terms of both automatic evaluation and human evaluation. Our code is available at https://github.com/siat-nlp/DDMN .

## introduction task-oriented dialog systems are designed to help users achieve specific goals with natural language, such as weather inquiry or restaurant reservation. compared with traditional pipeline methods @xcite , end-to-end approaches recently have gained much attention @xcite @xcite , since they free the task-oriented dialog systems from the manually designed pipeline modules and can be automatically scaled up to new domains. recently, sequence-to-sequence (seq2seq) models have dominated the study of end-to-end taskoriented dialog systems @xcite . different from typical seq2seq models for open-domain dialog systems, the successful conversations for task-oriented dialog systems heavily rely on both dialog history and domain-specific knowledge base (kb). to effectively incorporate kb information and perform knowledge-based reasoning, memory augmented models have been proposed @xcite , which model the dialog history and the kb knowledge as a bag of words in a flat memory. despite the remarkable progress of previous studies, current memory based models for multi-turn taskoriented dialog systems still suffer from the following limitations. first, existing methods concatenate dialog utterances of current turn and previous turns as a whole, which ignore previous reasoning process performed by the model and are incapable of dynamically tracking long-term dialog states. these methods introduce much noise since previous utterances as the context is lengthy and redundant @xcite . taking the dialog in to alleviate the aforementioned limitations, we propose a dual dynamic memory network (ddmn), which keeps track of the long-term dialog history and kb knowledge with separate memories. specifically, we leverage a dialog memory manager to effectively maintain history utterances with a dialog history memory and a dialog state memory. the dialog history memory keeps fixed to store the representation of dialog context throughout the whole conversation, and the dialog state memory keeps updated at each turn to track the flow of history information and capture proper information of current turn for generation. we leverage a kb memory manager containing a kb memory and a kb memory pointer to effectively track kb knowledge. the kb memory stores the kb triples using an end-to-end memory network @xcite and is shared across the entire conversation. the kb memory pointer softly attends to the kb memory at each turn, and guides the model to select appropriate kb entries in decoding. our main contributions can be summarized as follows. as shown in figure 1 , our proposed ddmn architecture consists of four components: a dialog encoder, a dialog memory manager, a kb memory manager, and a decoder. we elaborate on the proposed model in detail as below. ## related work end-to-end methods have shown promising results recently and attracted increasing attention since they are easily adapted to a new domain. some approaches view the dialog history as a sequence using recurrent neural networks @xcite , which also force the kb triples with the same pattern and make it hard to perform reasoning over them. to better handle kb triples in task-oriented dialogs, the memory network based architecture @xcite and its variants @xcite have been proposed and shown promising results. mem2seq @xcite and glmp @xcite further augmented memory based methods by incorporating copy mechanism @xcite , which enable the models copy words from past dialog utterances or from kb. these methods use a shared memory for the kb triples and the dialog utterances, making it difficult to reason over the memory and distinguish between the two forms of data. recently, there have been several works employing separate memories for modeling the dialog context and kb triples @xcite @xcite . for example, bossnet @xcite implicitly disentangled the language model from knowledge incorporation and thus enhance the ability of copying unseen kb entries. multi-level memory model @xcite represented the kb results with a multi-level memory instead of the form of triples. wmm2seq @xcite adopted a working memory to interact with a dialog context memory and a kb memory. nevertheless, existing methods still ignore the flow of history information during conversations, making it struggle to perform well in long-turn interactions. different from the aforementioned methods, we propose a dialog memory manager and a kb memory manager to dynamically track the dialog context and kb triples, respectively. ## conclusion in this paper, we propose a novel dual dynamic memory network (ddmn) with a dialog memory manager and a kb memory manager for multi-turn end-to-end task-oriented dialog systems. ddmn encodes dialog context turn by turn and the dialog memory manager dynamically tracks the dialog history. the kb memory manager shares the kb information throughout the whole conversation with a kb memory pointer to softly distill relevant kb entries at each turn. in addition, we leverage self-critical sequence training to boost the performance of ddmn. extensive experiments on three public dialog datasets demonstrate the superior performance of our model in both automatic and human evaluation.