Neural Dialogue State Tracking with Temporally Expressive Networks (2020)

Abstract: Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.

## introduction spoken dialogue systems (sds) connect users and computer applications through human-machine conversations. the users can achieve their goals, such as finding a restaurant, by interacting with a task-oriented sds over multiple dialogue rounds or turns. dialogue state tracking (dst) is an important task in sds and the key function is to maintain the state of the system so as to track the progress of the dialogue. in the context of this work, a state (or aggregated state) is the user's intention or interest accumulated from the conversation history, and the user's intention or interest at each turn is referred to as turn-level state. many neural-network models have been successfully applied to dst. these models usually solve the dst problem by two approaches, the implicit tracking and the explicit tracking. as is shown in figure 1 (a), the implicit tracking models @xcite @xcite @xcite employs recurrent networks to accumulate features extracted from historical system action and user * corresponding author utterance pairs. a classifier is then built upon these accumulated features for state prediction. although the implicit tracking captures temporal feature dependencies in recurrent-network cells, the state dependencies are not explicitly modeled. only considering temporal feature dependencies is insufficient for accurate state prediction. this fact has been confirmed via an ablation study in our experiment. unlike the implicit tracking, the explicit tracking approaches, such as nbt @xcite and glad @xcite , model the state dependencies explicitly. from the model structure in figure 1 (b), the explicit tracking approaches first build a classifier to predict the turn-level state of each turn and then utilize a state aggregator for state aggregation. despite achieving remarkable improvements upon the previous models, current explicit tracking models can be further improved in two aspects. one is that the temporal feature dependencies should be considered in model design. the explicit tracking models only extract features from the current system action and user utterance pair. in practice, the slot-value pairs in different turns are highly dependent. for example, if a user specifies (food, italian) at the current turn, he or she will probably not express it again in the future turns. for that reason, only extracting features from the current system action and user utterance pair is inadequate for turn-level state prediction. the other is that the uncertainties in the state aggregation can be more expressively modeled. the state-aggregation approaches in current explicit tracking models are sub-optimal. the deterministic rule in glad will propagate errors to future turns and lead to incorrect state aggregation. the heuristic aggregation in nbt needs further estimate the best configuration of its coefficient . an approach that can both reduce the error propagation and require less parameter estimation is necessary for the state aggregation. in this study, we propose a novel temporally expressive networks (ten) to jointly model the temporal feature dependencies and temporal state dependencies (figure 1 (c )). specifically, to improve the turn-level state prediction, we exploits hierarchical recurrent networks to capture temporal feature dependencies across dialogue turns. furthermore, to reduce state aggregation errors, we introduce factor graphs to formulate the state dependencies, and employ belief propagation to handle the uncertainties in state aggregation. evaluating on the dstc2, woz and multiwoz datasets, ten is shown to improve the accuracy of the turn-level state prediction and the state aggregation. the ten model establishes itself as a new state-of-the-art model on the dstc2 dataset and a state-of-the-art comparable model on the woz dataset. ## problem statement in a dialogue system, the state is represented as a set of slot-value pairs. let s denote the predefined set of slots. for each slot @xmath0, let v(s) denote the set of all possible values associated with slot s. we also include an additional token, unknown, as a legal value for all slots to represent their value is not determined. and we define let x denote the state space, and @xmath1. each state configuration x can be regarded as a function mapping x(s) from s to v * . for example, let x t denotes the state configuration of the t th dialogue turn, u t denotes the user utterance of the t th turn and a t denotes the system action based on previous state x t-1 . let y @xmath2, which is meant to capture the user intention of the current utterance. the system computes the aggregated state x t through a deterministic procedure, according to y t and x t-1 . we next describe this procedure. for any given s, we define an operator on v * (s) as follows. for any v, @xmath3, we then extend the operator to any two elements x, @xmath4, where x y is also an element in x (x y)(s) := x(s) y(s). for example, if x t-1 takes the configuration x in (1) and if y t is the dialogue process can be characterized by a random process {(x t , y t , a t , u t ) : @xmath5, 2, . . .)}. in the dst problem, the probability measure p which defines the dialogue process is unknown. we are however given a set r of realizations drawn from p, where each @xmath6, given in the form of {(x let @xmath7, x 2 , . . . , x t ) and assume similar notations for @xmath8, @xmath9. the learning problem for dst then becomes estimating p (x t |@xmath10, @xmath11. ## related works traditional works deal with the dst task using spoken language understanding (slu), including @xcite @xcite @xcite @xcite . joint modeling of slu and dst @xcite @xcite has also been presented and shown to outperform the separate slu models. models like @xcite incorporate statistical semantic parser for modeling the dialogue context. these models rely on hand-crafted features or delexicalisation strategies and are difficult to scale to realistic applications. recently, neural network models have been applied in the dst task, and there are mainly two model design approaches. one approach aggregates the features extracted from previous turns of the dialogue using recurrent neural networks, including statenet @xcite , largescale @xcite and sumbt @xcite . the other approach, like nbt @xcite and glad @xcite , build a model for predicting turn-level state, and estimate the state by accumulating all previous turn-level states. the design of ten integrates the advantages of both approaches. another topic related to our work is the markov decision process (mdp) and the factor graphs. several works define a dialogue system as a partially observable markov decision process (pomdp), including @xcite @xcite . in this paper, the definition of the dialogue process is related to the markov decision process. the factor graphs have been applied in many applications, such as social influence analysis @xcite , knowledge base alignment @xcite , entity linking @xcite and visual dialog generation @xcite . the factor graphs in these applications are used to integrate different sources of features or repre-sentations into a unified probabilistic model. in this paper, the factor graphs are naturally adopted to tackle the error propagation problem in state aggregation. ## concluding remarks our inspiration for ten comes from a careful study of the dialogue process. this allows us to lay out the dependency structure of the network as in figure 1 (c ), where the temporal feature dependencies and the temporal state dependencies are jointly modelled. the application of the belief propagation in this model allows an elegant combination of graphical models with deep neural networks. the proposed model may generalize to other sequence prediction tasks.