Abstract: Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users' patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The code is available at this https URL

Introduction: In a task-oriented dialogue system, the dialogue manager policy module predicts actions usually in terms of dialogue acts and domain specific slots. It is a crucial component that influences the efficiency (e.g., the conciseness and smoothness) of the communication between the user and the agent. Both supervised learning (SL) BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 and reinforcement learning (RL) approaches BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9 have been adopted to learn policies. SL learns a policy to predict acts given the dialogue state. Recent work BIBREF10, BIBREF11 also used SL as pre-training for RL to mitigate the sample inefficiency of RL approaches and to reduce the number of interactions. Sequence2Sequence (Seq2Seq) BIBREF12 approaches have also been adopted in user simulators to produce user acts BIBREF13. These approaches typically assume that the agent can only produce one act per turn through classification. Generating only one act per turn significantly limits what an agent can do in a turn and leads to lengthy dialogues, making tracking of state and context throughout the dialogue harder. An example in Table TABREF3 shows how the agent can produce both an inform and a multiple_choice act, reducing the need for additional turns. The use of multiple actions has previously been used in interaction managers that keep track of the floor (who is speaking right now) BIBREF14, BIBREF15, BIBREF16, but the option of generating multiple acts simultaneously at each turn for dialogue policy has been largely ignored, and only explored in simulated scenarios without real data BIBREF17. This task can be cast as a multi-label classification problem (if the sequential dependency among the acts is ignored) or as a sequence generation one as shown in Table TABREF4. In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18. We compare this model with baseline classifiers and sequence generation models and show that it consistently outperforms them.

Question: What is the source of external knowledge?
Answer: counts of predicate-argument tuples from English Wikipedia