Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022)

Abstract: Dialogue State Tracking (DST) aims to keep track of users’ intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel D ynamic S chema G raph F usion Net work ( DSGFNet ), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods.

## introduction task-oriented dialogue systems can help users accomplish different tasks @xcite , such as flight reservation, food ordering, and appointment scheduling. conventionally, task-oriented dialogue systems consist of four modules @xcite : natural language understanding (nlu), dialogue state tracking (dst), dialogue manager (dm), and natural language generation (nlg). in this paper, we will focus on the dst module. the goal of dst is to extract users' goals or intentions as dialogue states and keep these states updated over the whole dialogue. in order to track users' goals, we need to have a predefined domain knowledge referred to as a schema, which consists of slot figure 1 : an example of dst. given the schemata for all domains, the slot values are extracted from the user and system utterances (e.g., spans highlighted with the same color in the figure). the dialogue state of each turn is represented as a set of slot-value pairs. among the domains and slots, there are prior slot-domain membership relations which are expressed in the predefined schemata, and also dialogue-aware dynamic slot relations which depend on the dialogue context (e.g., coreference, co-update, and co-occurrence). many models have been developed for dst due to its importance in task-oriented dialogue systems. traditional approaches use deep neural networks or pre-trained language models to encode the dialogue context and infer slot values from it @xcite @xcite @xcite @xcite . these models predict slot values without considering the relations among domains and slots. however, domains and slots in a dialogue are unlikely to be entirely independent, and ignoring the relations among domains and slots may lead to sub-optimal perfor-mance. to address this issue, several recent works have been proposed to model the relations among domains and slots in dst. some of them introduce predefined schema graphs to incorporate prior slotdomain membership relations, which are defined based on human experience in advance @xcite . the others use an attention mechanism to capture dialogue-aware dynamic slot relations @xcite . the dialogue-aware dynamic relations are the logical relations of slots across domains, which are highly related to specific dialogue contexts. however, existing dst models that involve the relations among domains and slots suffer from two major issues: (1) they fail to fuse the prior slotdomain membership relations and dialogue-aware dynamic slot relations explicitly; and (2) they fail to consider their generalizability to new domains. in practical scenarios, task-oriented dialogue systems need to support a large and constantly increasing number of new domains. to tackle these issues, we propose a novel approach named dsgfnet (dynamic schema graph fusion network). for the first issue, dsgfnet dynamically updates the schema graph consisting of the predefined slot-domain membership relations with the dialogue-aware dynamic slot relations. to incorporate the dialogue-aware dynamic slot relations explicitly, dsgfnet adds three new edge types to the schema graph: co-reference relations, co-update relations, and co-occurrence relations. for the second issue, to improve its generalizability, dsgfnet employs a unified model containing schema-agnostic parameters to make predictions. specifically, our proposed dsgfnet comprises of four components: a bert-based dialogue utterance encoder to contextualize the current turn dialogue context and history, a bert-based schema graph encoder to generalize to unseen domains and model the prior slot-domain membership relations on the schema graph, a dialogue-aware schema graph evolving network to augment the dialogueaware dynamic slot relations on the schema graph, and a schema graph enhanced dialogue state decoder to extract value spans from the candidate elements considering the evolved schema graph. the contributions of this paper can be summarized as follows: ## related work recent dst approaches mainly focus on encoding the dialogue contexts with deep neural networks (e.g., convolutional and recurrent networks) and inferring the values of slots independently @xcite @xcite @xcite @xcite . with the prevalence of pretrained language models, such as bert @xcite and gpt-2 @xcite , a great variety of dst approaches have been developed on top of these pre-trained models @xcite . the relations among domains and slots are not considered in the above approaches. however, the prior slotdomain membership relations can facilitate the sharing of domain knowledge and the dialogueaware dynamic slot relations can conduce dialogue history understanding. ignoring these relations may lead to sub-optimal performance. to fill in this gap, several new dst approaches, which involve the relations among domains and slots, have been proposed. some of them leverage a graph structure to capture the slot-domain membership relations @xcite @xcite @xcite . specifically, a predefined schema graph is employed to represent the slot-domain membership relations. however, they fail to incorporate the dialogue-aware dynamic slot relations into the schema graph. the other approaches utilize the attention mechanism to learn dialogue-aware dynamic slot relation features in order to facilitate information flow among slots @xcite ; dialogue context schemata dialogue state bert-based dialogue utterance encoder current utterance previous utterances !"# @xmath0 & #'( @xmath1 * dialogue context … + ,-. dialogue utterance encoder + % + & + /01 + / (3 4 , 3 6 ) dialogue state decoder value prediction layer + ,-. 8 4 8 9 8 : 8 ; token embeddings schema-agnostic embedding initializer domain/slot description !"# @xmath2 & #'( < = domain/slot schema graph encoder restaurant movie taxi destination departure name price time location type evolved schema graph node embeddings (>, ? = ) @ ? = dynamic slot relation completion layer a(<, b) schema graph evolving @xmath3,1 e =,% e =,& e =,f weighted sum relation reasoning schema-dialogue fusion layer multi-head attention linear output restaurant movie taxi destination departure name price time location type to ke n em be dd in @xmath4 figure 2 : the architecture of dsgfnet, which contains a dialogue utterance encoder, a schema graph encoder, a schema graph evolving network, and a dialogue state decoder. @xcite @xcite . however, these approaches ignore the slot-domain membership relations defined by prior knowledge. since both the prior slot-domain membership relations and dialogue-aware dynamic slot relations can enhance dst performance, our approach is developed to combine them in an effective way. given that a deployed dialogue system may encounter an ever-increasing number of new domains that have limited training data available, the dst module should be capable of generalizing to unseen domains. recent dst approaches have focused on using zero-shot learning to achieve this goal @xcite . these approaches exploit the natural language descriptions of schemata to transfer knowledge across domains. however, they ignore the relations among domains and slots. in this work, we propose a unified framework to fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations, no matter whether the domains are seen or not. ## dynamic schema graph fusion network the proposed dsgfnet consists of four components: (1) a bert-based dialogue utterance encoder that aims to contextualize the tokens of the current turn and the dialogue history; ## results and discussion tables 2, 3, 4 show the performance of dsgfnet as well as the baselines on three datasets respectively. it is shown that dsgfnet achieves state-ofthe-art performance in unseen domains on sgd, all domains on sgd, and multiwoz2.2. all improvements observed compared to the baselines are statistically significant according to two sided paired t-test (p ¡ 0.05). and the performance on multiwoz2.1 are comparable with the state-ofthe-art foot_1 . most notably, dsgfnet improves the performance on sgd most significantly, which has unseen domains and more complex schemata domains, compared to the runner-up. it indicates that dsgfnet can facilitate knowledge transfer to new domains and improve relation construction among complex schemata domains. we conjecture that it is due to dsgfnet containing the schema-agnostic encoder and dynamic schema graph. the following analysis provides a better understanding of our model's strengths. @xcite 20.0% 25.4% fastsgt @xcite 20.8% 29.2% seq2seq-du @xcite 23.5% 30.1% dsgfnet 24.4% 32.1% ## conclusion we have proposed a new approach to dst, referred to as dsgfnet, which effectively fuses prior slotdomain membership relations and dialogue-aware dynamic slot relations on the schema graph. to incorporate the dialogue-aware dynamic slot relations into dst explicitly, dsgfnet identifies co-reference, co-update, and co-occurrence relations. to improve the generalization ability, ds-gfnet employs a schema-agnostic graph attention network to share information. experimental results show that dsgfnet outperforms the existing methods in dst on three benchmark datasets, including unseen domains of sgd, all domains of sgd, mul-tiwoz2.1, and multiwoz2.2. for future work, we intend to further enhance our approach by utilizing more complex schemata and data augmentation techniques.