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DECAF: A Modular and Extensible Conversational Search Framework

Published: 18 July 2023 Publication History

Abstract

The Conversational Search (CS) paradigm allows for an intuitive interaction between the user and the system through natural language sentences and it is increasingly being adopted in various scenarios. However, its widespread experimentation has led to the birth of a multitude of conversational search systems with custom implementations and variants of information retrieval models. This exacerbates the reproducibility crisis already observed in several research areas, including Information Retrieval (IR). To address this issue, we propose DECAF: a modular and extensible conversational search framework designed for fast prototyping and development of conversational agents. Our framework integrates all the components that characterize a modern conversational search system and allows for the seamless integration of Machine Learning (ML) and Large Language Models (LLMs)-based techniques. Furthermore, thanks to its uniform interface, DECAF allows for experiments characterized by a high degree of reproducibility. DECAF contains several state-of-the-art components including query rewriting, search functions under BoW and dense paradigms, and re-ranking functions. Our framework is tested on two well-known conversational collections: TREC CAsT 2019 and TREC CAsT 2020 and the results can be used by future practitioners as baselines. Our contributions include the identification of a series of state-of-the-art components for the conversational search task and the definition of a modular framework for its implementation.

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    SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2023
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    1. conversational search
    2. decaf
    3. information retrieval
    4. modular framework

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    • (2024)Who Will Evaluate the Evaluators? Exploring the Gen-IR User Simulation SpaceExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_11(166-171)Online publication date: 9-Sep-2024
    • (2024)Simulating Follow-Up Questions in Conversational SearchAdvances in Information Retrieval10.1007/978-3-031-56060-6_25(382-398)Online publication date: 24-Mar-2024

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