Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue state according to a dialogue policy module, after which a machine response is generated. Traditionally, a dialogue state consists of a set of requests and joint goals, both of which are represented by a set of slot-value pairs (e.g. ( request, phone), ( area, north), ( food, Japanese)) (Henderson et al., 2014). In a recently proposed multi-DST Models ITC NBT -CNN (Mrksic et al., 2017) O (mn) MD-DST (Rastogi et al., 2017) O (n) GLAD (Zhong et al., 2018) O (mn) StateNet PSI (Ren et al., 2018) O (n) TRADE (Wu et al., 2019) O (n) HyST (Goel et al., 2019) O (n) DSTRead (Gao et al., 2019) O (n) Table 1: The Inference Time Complexity (ITC) of previous DST models. The ITC is calculated based on how many times inference must be performed to complete a prediction of the belief state in a dialogue turn, where m is the number of values in a predefined ontology list and n is the number of slots.