Question: What does the DST consist of?
Evidence: Spoken dialogue systems, or conversational systems, are designed to interact and assist users using speech and natural language to achieve a certain goal BIBREF0. A consolidated approach to build a task-oriented dialogue system involves a pipeline architecture (see Figure FIGREF1), where each component is trained to perform a sub-task, and the combination of the modules in a given sequence aims at handling the complete task-oriented dialogue. In such a pipeline, a spoken language understanding (SLU) module determines the user's intent and the relevant information that the user is providing represented in terms of slot-value pairs. Then, the dialogue state tracker (DST) uses the information of the SLU together with the past dialogue context and updates its belief state BIBREF1, BIBREF2. In this framework a dialogue state indicates what the user requires at any point in the dialogue, and it is represented as a probability distribution over the possible states (typically a set of pre-defined slot-value pairs specific of the task). The dialogue policy manager, then, decides on the next system action based on the dialogue state. Finally, a natural language generation (NLG) component is responsible for the generation of an utterance that is returned as response to the user.
In this paper we focus on the dialogue state tracker component, whose role is to track the state of the dialogue based on the current user utterance, the conversation history and any additional information available to the system BIBREF0. Deep neural network techniques, such as recurrent neural network and convolutional neural networks, are the current state-of-the-art models for DST BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, showing high capacity to generalize from annotated dialogues. As an example, the GLAD model (Global-Locally Self-Attentive Dialogue State Tracker - BIBREF8), employs independent slot-specific encoders, consisting of a recurrent and a self-attention layer, for each of the user utterance, the system action and the slot-value pairs. Another DST system, GCE (Globally Conditioned Encoder - BIBREF9), simplifies the GLAD neural architecture removing the slot-specific recurrent and self-attention layers of the encoder, but still requires separate encoders for the utterance, the system action and the slot-values.
Although the neural network models mentioned above achieve state-of-the-art performance, the complexity of their architectures make them highly inefficient in terms of time complexity, with a significant latency in their prediction time. Such latency may soon become a serious limitation for their deployment into concrete application scenarios with increasing number of slots, where real time is a strong requirement. Along this perspective, this work investigates the time complexity of state-of-the-art DST models and addresses their current limitations. Our contributions are the following:
we have designed and implemented an efficient DST, consisting of a Global encoder and Slot-Attentive decoders (G-SAT);
we provide empirical evidences (three languages of the WOZ2.0 dataset BIBREF6) that the proposed G-SAT model considerably reduces the latency time with respect to state-of-art DST systems (i.e. over 15 times faster), while keeping the dialogue state prediction inline with such systems;
further experiments show that the proposed model is highly robust when either pre-trained embeddings are used or when they are not used, in this case outperforming state-of-art systems.
The implementation of the proposed G-SAT model is publicly available.
The paper is structured as follows. Section 2 summarizes the main concepts behind the definition of dialogue state tracking. Section 3 reports the relevant related work. Section 4 provides the details of the proposed G-SAT neural model, and, finally, Section 5 and 6 focus on the experiments and the discussion of the results we achieved.
Our contributions are the following: we have designed and implemented an efficient DST, consisting of a Global encoder and Slot-Attentive decoders (G-SAT); we provide empirical evidences (three languages of the WOZ2.0 dataset) that the proposed G-SAT model considerably reduces the latency time with respect to state-of-art DST systems (i.e. over 15 times faster), while keeping the dialogue state prediction inline with such systems; further experiments show that the proposed model is highly robust when either pre-trained embeddings are used or when they are not used, in this case outperforming state-of-art systems.
Answer: It consists of a Global encoder and Slot-Attentive decoders (G-SAT).