Amendable Generation for Dialogue State Tracking (2021)

Abstract: In task-oriented dialogue systems, recent dialogue state tracking methods tend to perform one-pass generation of the dialogue state based on the previous dialogue state. The mistakes of these models made at the current turn are prone to be carried over to the next turn, causing error propagation. In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass. With the additional amending generation pass, our model is tasked to learn more robust dialogue state tracking by amending the errors that still exist in the primitive dialogue state, which plays the role of reviser in the double-checking process and alleviates unnecessary error propagation. Experimental results show that AG-DST significantly outperforms previous works in two active DST datasets (MultiWOZ 2.2 and WOZ 2.0), achieving new state-of-the-art performances.

## introduction dialogue state tracking (dst) is a crucial task in task-oriented dialogue systems, as it affects database query results as well as the subsequent policy prediction @xcite . it extracts users' goals at each turn of the conversation and represents them in the form of a set of (slot, value) pairs, i.e., dialogue state. traditional methods of dst mainly rely on a predefined ontology which includes all possible slots and corresponding values. these models predict the value for each slot as a classification problem @xcite @xcite . however, in practical applications, some slot values appearing in the conversations cannot be predefined, and it is infeasible to acquire a fully predefined ontology or transfer to other domains with fixed predefined ontology. to address such challenges, open-vocabulary dst has been proposed, where the value of each slot is directly generated or extracted from the dialogue history @xcite @xcite . although this approach offers scalability and is capable of handling unseen slot values, many of the previous models are not efficient enough as they need to predict the dialogue state from scratch based on the dialogue history. on the merits of utilizing the previous dialogue state as a compact representation of the previous dialogue history, some recent methods choose to take the previous dialogue state into consideration when generating the slot values. one direction is to decompose dst into two explicit sub-tasks: state operation prediction and value generation @xcite . at each turn, whether or how to modify the value in the previous dialogue state is determined by the discrete operations from the state operation prediction, so the accuracy of state operation prediction holds back the overall dst performance @xcite . another direction of recent works recasts dialogue state tracking into a single causal language model by using the dialogue of the current turn and the previous dialogue state as input sequence @xcite , where the current dialogue state is generated by jointly modeling the state operation prediction and value generation in a implicit fashion. while it is more effective and reasonable to use the previous dialogue state under the markov assumption, the mistakes of these models made during the prediction of the current turn are prone to be carried over to the next turn, causing error propagation. these carried-over mistakes are unlikely to be fixed in the next turn. essentially, these models perform a one-pass generation process and lack a double-checking process to amend the mistakes of ok, and what day and time would you like that reservation? i would like to make a reservation for saturday at 11:45. and there has been a change in plans, i will be dining alone. ## methodology in this section, we introduce ag-dst in the following aspects: the basic generation, the amending generation and the training objective. ## conclusion in this paper, we propose a novel amendable generative approach for dialogue state tracking, which learns implicit state operation prediction and value generation jointly in a single model to reduce the error propagation. meanwhile, our model offers an opportunity to amend the primitive dialogue state in the amending generation. experimental results show that our model outperforms previous works on both multiwoz 2.2 and woz 2.0 datasets, achieving state-of-the-art performance. the ablation study and attention visualization demonstrate that the proposed amending generation is significantly effective. moreover, we analyze the types of mistakes that can be resolved by the amending generation and provide some examples to illustrate them in the appendix. in the future, we will try to integrate schema descriptions into our architecture and explore a generative approach to support end-to-end task-oriented dialogue system.