Natural Language Task-Oriented Dialog System 2.0

• URL: http://arxiv.org/abs/2407.15055v1
• Date: Sun, 21 Jul 2024 04:52:38 GMT
• Title: Natural Language Task-Oriented Dialog System 2.0
• Authors: Adib Mosharrof, A. B. Siddique,
• Abstract summary: Task-oriented dialog (TOD) systems play a crucial role in facilitating efficient interactions between users and machines. These systems traditionally rely on manually annotated metadata, such as dialog states and policy annotations. We introduce Natural Language Task Oriented Dialog System (NL-ToD), a novel model that removes the dependency on manually annotated turn-wise data.
• Score: 2.757798192967912
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Task-oriented dialog (TOD) systems play a crucial role in facilitating efficient interactions between users and machines by focusing on achieving specific goals through natural language communication. These systems traditionally rely on manually annotated metadata, such as dialog states and policy annotations, which is labor-intensive, expensive, inconsistent, and prone to errors, thereby limiting the potential to leverage the vast amounts of available conversational data. A critical aspect of TOD systems involves accessing and integrating information from external sources to effectively engage users. The process of determining when and how to query external resources represents a fundamental challenge in system design, however existing approaches expect this information to provided in the context. In this paper, we introduce Natural Language Task Oriented Dialog System (NL-ToD), a novel model that removes the dependency on manually annotated turn-wise data by utilizing dialog history and domain schemas to create a Zero Shot Generalizable TOD system. We also incorporate query generation as a core task of the system, where the output of the system could be a response to the user or an API query to communicate with an external resource. To achieve a more granular analysis of the system output, we classify the output into multiple categories: slot filling, retrieval, and query generation. Our analysis reveals that slot filling is the most challenging TOD task for all models. Experimental results on three popular TOD datasets (SGD, KETOD and BiToD) shows the effectiveness of our approach as NL-ToD outperforms state-of-the-art approaches, particularly with a \textbf{31.4\%} and \textbf{82.1\%} improvement in the BLEU-4 score on the SGD and KETOD dataset.

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