Schema Encoding for Transferable Dialogue State Tracking (2022)

Abstract: Dialogue state tracking (DST) is an essential sub-task for task-oriented dialogue systems. Recent work has focused on deep neural models for DST. However, the neural models require a large dataset for training. Furthermore, applying them to another domain needs a new dataset because the neural models are generally trained to imitate the given dataset. In this paper, we propose Schema Encoding for Transferable Dialogue State Tracking (SET-DST), which is a neural DST method for effective transfer to new domains. Transferable DST could assist developments of dialogue systems even with few dataset on target domains. We use a schema encoder not just to imitate the dataset but to comprehend the schema of the dataset. We aim to transfer the model to new domains by encoding new schemas and using them for DST on multi-domain settings. As a result, SET-DST improved the joint accuracy by 1.46 points on MultiWOZ 2.1.

## introduction the objective of task-oriented dialogue systems is to help users achieve their goals by conversations. dialogue state tracking (dst) is the essential subtask for the systems to perform the purpose. users may deliver the details of their goals to the systems during the conversations, e.g., what kind of food they want the restaurant to serve and at what price level they want to book the hotel. thus, the systems should exactly catch the details from utterances. they should also communicate with other systems by using apis to achieve users' goals, e.g., to search restaurants and to reserve hotels. the goal of dst is not only to classify the users' intents but also to fill the details into predefined templates that are used to call apis. recent work has used deep neural networks for dst with supervised learning. they have improved the accuracy of dst; however, they require a large dataset for training. furthermore, they need a new dataset to be trained on another domain. unfortunately, the large dataset for training a dst model is not easy to be developed in real world. the motivation of supervised learning is to make deep neural networks imitate humans. but, they actually imitate the given datasets rather than humans. someones who have performed hotel reservation work could easily perform restaurant reservation work if some guidelines are provided, but neural models may have to be trained on a new dataset of the restaurant domain. the difference between humans and neural models is that humans can learn how to read guidelines and to apply the guidelines to their work. this is why transfer learning is important to train neural models on new domains. in this paper, we propose schema encoding for transferable dialogue state tracking (set-dst), which is a neural dst method with transfer learning by using dataset schemas as guidelines for dst. the motivation of this study is that humans can learn not only how to do their work, but also how to apply the guidelines to the work. we aim to make a neural model learn how to apply the schema guidelines to dst beyond how to fill predefined slots by simply imitating the dataset on multi-domain settings. the schema includes metadata of the dataset, e.g., which domains the dataset covers and which slots have to be filled to achieve goals. set-dst has a schema encoder to represent the dataset schema, and it uses the schema representation to understand utterances and to fill slots. recently, transfer learning has been becoming important because development of new datasets is costly. transfer learning makes it possible to pre-train neural models on large-scale datasets to effectively finetune the models on small-scale downstream tasks. we used sgd @xcite as the largescale dataset, and evaluated set-dst on multi-woz 2.1 @xcite , which is a standard benchmark dataset for dst, as the downstream task. set-dst achieved state-of-the-art accuracy schema encoder state generator (gpt-2) dataset schema ... ... (a) schema encoding for active slots and intents classification. ... ... state generator (gpt-2) ... ... ... (b) dialogue state generation. figure 1: overview of set-dst. the schema encoder takes the dataset schema and generates slot vectors and intent vectors. the state generator takes the previous dialogue state d t-1 and the dialogue history h t to calculate active scores of slots and intents. f is an score function to calculate whether the slots or intents are activated on turn t. then, the state generator additionally takes the activated slots and intents to generate the current dialogue state d t . s t indicates the activated slots and i t indicates the activated intents. on the downstream dst task. we further confirmed that set-dst worked well on the small downstream dataset. this result demonstrates that transfer learning with schema encoding improves the performance of neural dst models and the efficiency of few-shot learning on dst. ## related work traditional dst models extract semantics by using natural language understanding (nlu) modules to generate dialogue states @xcite . the limitation of these models is that they rely on features extracted by humans. recent work has focused on building end-to-end dst models without hand-crafted features. @xcite use global modules to share parameters between different slots. @xcite improve the latency by removing inefficient recurrent layers. transferable dst models that can be adapted to new domains by removing the dependency on the domain ontology are proposed @xcite . @xcite attempt to solve dst as a question answering task using knowledge graph. more recently, large-scale pre-trained language models such as bert @xcite and gpt-2 @xcite are used for dst. the pre-trained bert acts as an nlu module to understand utterances @xcite @xcite . gpt-2 makes it possible to solve dst as a conditional language modeling task @xcite . @xcite propose the baseline method that defines the schema of dataset and uses it for training and inference. a drawback of them is that the calculation cost is high because they use the domain ontology and access all values to estimate the dialogue state. dst models that uses schema graphs to encode the relation between slots and values are proposed @xcite . however, they focus on encoding the relation between slots and values of the given domains not on adaptation to new domains. in this paper, we focus on making the model learn how to understand the schema and how to apply it to estimate the dialogue state, not just on encoding the in-domain relation. ## schema encoding for transferable dialogue state tracking in this section, we describe the architecture of set-dst and how to optimize it. figure 1 shows the overview of our method. the model consists of the schema encoder and the state generator. set-dst generates the dialogue state in two steps: (a) schema encoding and classification, and (b) dialogue state generation. in this paper, we define some terms as follows. schema metadata of the dataset, e.g., what domains, services, slots, and intents the dataset covers. a dataset has a schema that describes the dataset. domain what domains the conversation goes on, e.g., restaurant, hotel, and attraction. a conversation can go on multiple domains. service_name: restaurants_1 description: a leading provider for restaurant search and reservations slot_name: restaurant_name description: name of the restaurant slot_name: price_range description: price range for the restaurant ... intent_name: reserverestaurant description: reserve a table at a restaurant intent_name: findrestaurants description: find a restaurant of a particular cuisine in a city service what services the system provides to users. it is similar to domain, but application-level. for example, restaurant domain can have two different services: (1) a service for searching and reserving restaurants and (2) a service focused on searching and comparing restaurants. in real world, a service corresponds to an application. action abstract actions of users to achieve their goals during conversations, e.g., to inform the system their requirements or to request the system for some information. appendix b demonstrates the details of the user actions covered in this paper. ## experiments in this section, we describe our experiments including the datasets, evaluation metric, and results. ## discussion schema encoding in this study, our goal is to transfer a pre-trained dst model to a low-resource domain without limiting the transference as language model level by using schema encoding. we pre-trained set-dst on sgd which is a relatively large dataset and fine-tuned it on multiwoz to transfer the schema encoding. as a result, the pre-training significantly improved the accuracy on dst. intent on fine-tuning in this paper, we define the intents as sub-goals to be achieved through a service. sgd has a schema for dialogues between a virtual assistant and an user. thus, it is assumed that a system achieves the user's sub-goals by using apis, and an intent corresponds to an api. virtual assistant should tackle various services that could consist of one more intents, e.g., to check account balance and to transfer money in bank service. unlike that, multiwoz has no schema and considers no apis as intents. thus, the schema that we temporarily created for experiments in the same form as the schema of sgd could cause confusion in generation of dialogue state. we believe that this is why the results without intent were slightly higher in the experiments. another reason would be the incorrect labels for intents that we automatically created for experiments on multiwoz. the joint accuracy that has been proposed as an evaluation metric for dst considers only slot-value pairs. however, task-oriented dialogue systems should call apis of external systems to achieve goals, e.g., to search restaurants and to reserve hotels. the systems that predict only slot-value pairs would be insufficient to replace rule-based traditional systems in real-world. even though use of intents made no improvement in joint accuracy, we believe that encoding the schema including intents is meaningful in terms of approaching more realistic dst. backbone model we used bert for schema encoding and gpt-2 for classification and generation. using only gpt-2 was an option in our study. however, we fixed the pre-trained bert during optimization to preserve the broad and general knowledge learned on pre-training and to use the knowledge for encoding given schema. bert is an excellent backbone model to encode text for downstream tasks; thus, we used bert instead of gpt-2 for the schema encoding. ## conclusion transfer learning that makes it possible to apply a pre-trained model to new domains has been attempted a lot. however, the attempts for dst have been just to use large-scale pre-trained models as language models. in this paper, we have proposed set-dst, which is an effective method for dst with transfer learning by using schema encoding. we have demonstrated how to encode the schema for transferable dst and how to use the schema representation for dialogue state generation. our experiments show that the schema encoding improves joint accuracy even in few-shot settings. even though our approach could perform dst well on target domain with few-shot settings, it required some new data to be fine-tuned. as part of our future work, we plan to design a dst model for zero-shot settings.