GCDST : A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking (2020)

Abstract: As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems. For practical usages, a major challenge to constructing a robust DST model is to process a conversation with multi-domain states. However, most existing approaches trained DST on a single domain independently, ignoring the information across domains. To tackle the multi-domain DST task, we first construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains. Then, we encode the graph information of dialogue states by graph convolutional networks and utilize a hard copy mechanism to directly copy historical states from the previous conversation. Experimental results show that our model improves the performances of the multi-domain DST baseline (TRADE) with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets, respectively.

## introduction a task-oriented dialogue system provides fundamental technologies for continuous interactions with a human to accomplish predefined specific goals, such as taxi reservation or hotel booking. dialogue state tracking (dst) is a crucial component in the task-oriented dialogue system. users' intentions and goals are extracted from the current utterances and the conversation history. then, the dst model encodes the information as a set of states to help dialogue systems to determine which actions should be taken in next steps @xcite . ## method figure 2 illustrates the encoder-decoder framework of our graph-based and copy-augmented multidomain dialogue state tracker (gcdst). different from the previous work @xcite , we introduce state graph representations into both the encoder and the decoder to model the associated information between dialogue states across domains. in addition, we propose a hard copy mechanism in dialogue decoder to get the history states from the last prediction. the framework consists of four main components. ## conclusion this paper presents a graph-based and copyaugmented multi-domain dst model (gcdst). in particular, gcdst constructs a graph to transfer knowledge among states with the same slots or values across domains by gcn and encodes the history utterances and states by two independent encoders. furthermore, we add the hard copy mechanism to directly copy states from the last turn for the decoder. empirical studies on the multiwoz 2.0 / 2.1 dialogue datasets suggest that gcdst outperforms previous systems substantially for the multi-domain dst task. further analysis demonstrates the positive effects of graph representations for information transferring across domains and advantages of copy mechanisms for state tracking of long-distance dialogue history.