KILDST : Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information (2022)

Abstract: Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. In addition, it is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. So, we introduce a new task - DST from dialogue between users about scheduling an event (DST-USERS). The DST-USERS task is much more challenging since it requires the model to understand and track dialogue states in the dialogue between users, as well as to understand who suggested the schedule and who agreed to the proposed schedule. To facilitate DST-USERS research, we develop dialogue datasets between users that plan a schedule. The annotated slot values which need to be extracted in the dialogue are date, time, and location. Previous approaches, such as Machine Reading Comprehension (MRC) and traditional DST techniques, have not achieved good results in our extensive evaluations. By adopting the knowledge-integrated learning method, we achieve exceptional results. The proposed model architecture combines gazetteer features and speaker information efficiently. Our evaluations of the dialogue datasets between users that plan a schedule show that our model outperforms the baseline model.

## introduction dst is a task to determine the final dialogue states by continuously tracking the dialogue between the user and the system. it is a challenging and essential task because it can be applied to many real-world applications, such as voice assistant systems. many approaches have been proposed to solve the dst problem @xcite . mintl @xcite ) framework adopts plug-and-play architecture to the pre-trained seq2seq model and can learn dst and nlu at the same time. som-dst @xcite updates the dialogue state in two steps: state operation prediction such as add, update and delete operation and dialogue state updater. the approaches have the advantage of improving performance; however, the error gets propagated to the dialogue state tracking phase of the model if an error occurs in the prediction of state operation. meanwhile, in the past few years, many innovative models in the field of mrc. among them, the mainstream approach formalizes reading comprehension to the extent of extracting answers from a given text @xcite @xcite @xcite @xcite @xcite . there have been various attempts to apply this promising mrc technique to the dst field, and it has shown remarkable performance @xcite . despite much research on dst, there are still some challenging problems to be solved. newlycoined words and unseen words pose problems @xcite . some slots, such as specific store names and movie names, are not general noun phrases and are more complicated to recognize @xcite . to overcome this problem, approaches for integrating external knowledge, such as gazetteer information or knowledge based on neural architectures, have been highlighted and studied again. one-hot vectors are typically used as inputs to the gazetteer encoder and are then concatenated with word representations. however, for some problems, simply integrating gazetteer information will not improve or reduce performance for some slots @xcite . in this paper, we conduct a new and challenging task that tracks the dialogue state between two users rather than the dialogue state tracking in the dialogue between users and systems, like in previous studies. ultimately, this model can be applied to a recommendation service on a smartphone that extracts and recommends a schedule for whatsapp, wechat, kakaotalk, and telegram. even if ambiguous slots are mentioned in the dialogue, the system cannot request explicit confirmation from the user, so it is more difficult to extract accurate information. several studies use a gazetteer to improve performance @xcite to solve this problem. especially, gemnet @xcite further enhances performance with effective gated gazetteer representations. we propose a novel architecture that effectively learns the gazetteer and speaker information to extract slots more accurately. the contributions of our work are as follows: ## proposed model we propose an effective knowledge-integrated learning method for dialogue state tracking using gazetteer and speaker information (kildst). as shown in figure 1 , the proposed model consists of 3 different sub-encoders, 1) a contextual dialogue encoder for encoding dialogue, 2) a contextual gazetteer encoder for encoding domain knowledge, and 3) a speaker encoder for encoding speaker information. our proposed knowledgeintegrated learning method efficiently consolidates information from multiple encoders and extracts the slots related to a schedule, such as a date, time, and location using span detectors. ## conclusions this paper presents a novel model architecture that effectively learns the gazetteer knowledge and speaker information for dst. our model consists of 3 different sub-encoders a contextual dialogue encoder for encoding dialogue, a contextual gazetteer encoder for encoding domain knowledge, and a speaker encoder for encoding speaker information. the knowledge-integrated learning method we proposed outperforms other models by integrating the information of various encoders more efficiently and accurately extracting slots. in addition, we have collected and created new dialogue datasets between users that plan a schedule. our evaluation of this dialogue dataset shows improvement over the state-of-the-art model by better extracting the schedule from a dialogue containing difficult words such as newly coined words and abbreviations. our model can be applied to a messenger app such as whatsapp, wechat, kakaotalk, and telegram as a recommendation system, extracting a schedule from dialogue and recommending a schedule to the user. in the future, we plan to expand our proposed model architecture to a model that extracts another meaningful event from a dialogue among more users.