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- W4384640988 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." @default.
- W4384640988 created "2023-07-19" @default.
- W4384640988 creator A5064703033 @default.
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- W4384640988 creator A5079246354 @default.
- W4384640988 date "2023-07-18" @default.
- W4384640988 modified "2023-09-26" @default.
- W4384640988 title "DECAF: A Modular and Extensible Conversational Search Framework" @default.
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- W4384640988 doi "https://doi.org/10.1145/3539618.3591913" @default.
- W4384640988 hasPublicationYear "2023" @default.
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