Abstract: End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset, and outperform two commercially deployed customer-facing dialog systems.

Introduction: Task-oriented dialog systems help a user to accomplish some goal using natural language, such as making a restaurant reservation, getting technical support, or placing a phonecall. Historically, these dialog systems have been built as a pipeline, with modules for language understanding, state tracking, action selection, and language generation. However, dependencies between modules introduce considerable complexity – for example, it is often unclear how to define the dialog state and what history to maintain, yet action selection relies exclusively on the state for input. Moreover, training each module requires specialized labels. Recently, end-to-end approaches have trained recurrent neural networks (RNNs) directly on text transcripts of dialogs. A key benefit is that the RNN infers a latent representation of state, obviating the need for state labels. However, end-to-end methods lack a general mechanism for injecting domain knowledge and constraints. For example, simple operations like sorting a list of database results or updating a dictionary of entities can expressed in a few lines of software, yet may take thousands of dialogs to learn. Moreover, in some practical settings, programmed constraints are essential – for example, a banking dialog system would require that a user is logged in before they can retrieve account information. This paper presents a model for end-to-end learning, called Hybrid Code Networks (HCNs) which addresses these problems. In addition to learning an RNN, HCNs also allow a developer to express domain knowledge via software and action templates. Experiments show that, compared to existing recurrent end-to-end techniques, HCNs achieve the same performance with considerably less training data, while retaining the key benefit of end-to-end trainability. Moreover, the neural network can be trained with supervised learning or reinforcement learning, by changing the gradient update applied. This paper is organized as follows. Section "Model description" describes the model, and Section "Related work" compares the model to related work. Section "Supervised learning evaluation I" applies HCNs to the bAbI dialog dataset BIBREF0 . Section "Supervised learning evaluation II" then applies the method to real customer support domains at our company. Section "Reinforcement learning illustration" illustrates how HCNs can be optimized with reinforcement learning, and Section "Conclusion" concludes.

Question: Does this paper propose a new task that others can try to improve performance on?
Answer: No, there has been previous work on recognizing social norm violation.