A G lobal P ointer based Robust Approach for Information Extraction from Dialog Transcripts (2022)

Abstract: With the widespread popularisation of intelligent technology, task-based dialogue systems (TOD) are increasingly being applied to a wide variety of practical scenarios. As the key tasks in dialogue systems, named entity recognition and slot filling play a crucial role in the completeness and accuracy of information extraction. This paper is an evaluation paper for Sere-TOD 2022 Workshop challenge (Track 1 Information extraction from dialog transcripts). We proposed a multi-model fusion approach based on GlobalPointer, combined with some optimisation tricks, finally achieved an entity F1 of 60.73, an entity-slot-value triple F1 of 56, and an average F1 of 58.37, and got the highest score in SereTOD 2022 Workshop challenge

## introduction task-oriented dialogue (tod) systems are designed for specific application areas and have gained more and more attention in both academia and industry recently @xcite . as a branch of the dialogue systems, tod systems are different from question-and-answer (qa) systems and chat-oriented dialogue systems. tod system needs to determine the user's intent through understanding, analysis, information extraction, and clarification. then complete a round of dialogue through natural language generation or apis. according to the work of zhao et al. @xcite and zhang et al. @xcite , the structure of a traditional tod system is shown in figure 1 , which can be divided into three modules, spoken language understanding (slu), natural language generation (nlg), and dialogue manager. the slu module converts language into semantic representations, the purpose is to obtain the semantic information of user input speech. the 1 https://docs.google.com/spreadsheets/d/1w28akkg6w jmuo15qlrlryrnv859mt1ry0chv8tfxy9o/edit?@xmath0. the task of this module is to decide how the system responds to the input speech @xcite ) and then the system updates its internal state, and then the system determines the system behaviour through policies. in order to provide information to the user, the dialogue manager usually needs to query the knowledge base or the internet, and it also needs to consider the historical data in the multi-round dialogue. finally, the nlg module translates the decisions of the system into natural language-based dialogues. among them, the state variables contain variables that track the dialogue process, as well as slots that represent user needs. ## approach in this paper, we focus on the baseline @xcite and practical business difficulties of and dialogue system, and propose suitable solutions. the difficulties can be summarised as follows: according to the above-mentioned difficulties, our solutions can be summarised as follows: ## model fusion in terms of data selection and split-folding strategy, we merge the original training data and dev data, and split them into four folds for training through kfold. in ## conclusion in this challenge, we use the globalpointer-based structure and probabilistic average fusion of roformer, deberta, roberta, macbert, and nezha as the main solution. at the same time, we adopted tricks such as adding global context information and breaking down context by character in the following step to further optimise the results. finally, we end up with an entity f1 of 60.73, an entity-slot-value triple f1 of 56, and an average f1 of 58.37, and got the highest average f1 score in the challenge of seretod 2022 workshop.