Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (2022)

Abstract: Collecting dialogue data with domain-slot-value labels for dialogue state tracking (DST) could be a costly process. In this paper, we propose a novel framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST. Specifically, we design an extraction module to extract domain-slot related verbs and nouns in the dialogue. Then, we integrates them into the description, which aims to prompt the model to identify the slot information. Furthermore, we introduce a random sampling strategy to improve the domain generalization ability of the model. We utilize a pre-trained model to encode contexts and description and generates answers with an auto-regressive manner. Experimental results show that our approaches substantially outperform the existing few-shot DST methods on MultiWOZ and gain strong improvements on the slot accuracy comparing to existing slot description methods.

## introduction dialogue state tracking (dst) is an essential component in a task-oriented dialogue system. it aims to keep track of users' domains, intents and slots information at each turn of the conversation, which helps to provide sufficient information for selecting the next system operation @xcite . recent neural-based dst models @xcite @xcite @xcite have made significant progress under the availability of large-scale labeled data. however, due to the high cost of data annotation and the lack of sufficient data in some specific domains, the performance of the general model will drop significantly. therefore, lack of generalization to new domains hinder the further application of task-oriented dialogue systems in practical industrial scenarios. user: i want to book at the allenbell for 4 nights for 8 people. user: i want to book at the allenbell for 4 nights for 8 people. current works mainly adopt two ways to deal with dst tasks in few-shot cross-domain scenario: in this work, we proposed a simple but efficient framework named domain-slot related information awareness method (dria) based on the domain-slot related keywords extraction module and a random sampling strategy. specifically, for the extraction module, we first use tf-idf algorithm @xcite and corenlp (manning et al., 2014) pos-tagging tool to extract several verbs and nouns in the dialogue as keywords, then we integrate them into our description. we believe that nouns and verbs always imply the topic or domain-slot related information in the dialogue. as shown in figure 1 , the user query is " i want to book at allenbel for 4 nights for 8 people.". one of the user's intention is to tell the agent how long to stay in the hotel, which can be expressed as "book hotel for 4 nights". the verb "book" and noun "nights" imply the domain-slot: hotelbook stay. similarly, "book", "nights" and "people" also imply the domain-slot: hotel-book people. further, the random sampling strategy is designed for solving the problem of simplistic description. we extract keywords by random sampling during the training to ensure that the description of every domain-slot will become more informative each turn. during the evaluation, we inject all the extracted keywords into the description to provide the model with as much information as possible. our contributions are summarized as follows:(1) we propose an effective framework to construct domain-slot related keywords descriptions. to the best of our knowledge, we are the first to incor-porate keywords information into the dst task. ## conclusion in this paper, we propose a simple but effective framework to tackle the few-shot cross-domain dst challenge. specifically, we propose dria based on t5. this framework incorporates the domain-slot related information into the description to help the model distinguish the domain-slot more clearly. further, we propose a random sampling strategy which enriches the content of description during training to improve the domain generalization ability of the model. results on multiwoz dataset show that our method outperforms most of the previous methods in the cross-domain few-shot dst settings.