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  4. Dynamic composition of question answering pipelines with Frankenstein
 
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2018
Conference Paper
Title

Dynamic composition of question answering pipelines with Frankenstein

Abstract
Question answering (QA) systems provide user-friendly interfaces for retrieving answers from structured and unstructured data given natural language questions. Several QA systems, as well as related components, have been contributed by the industry and research community in recent years. However, most of these efforts have been performed independently from each other and with different focuses, and their synergies in the scope of QA have not been addressed adequately. FRANKENSTEIN is a novel framework for developing QA systems over knowledge bases by integrating existing state-of-the-art QA components performing different tasks. It incorporates several reusable QA components, employs machine learning techniques to predict best performing components and QA pipelines for a given question, and generates static and dynamic executable QA pipelines. In this paper, we illustrate different functionalities of FRANKENSTEIN for performing independent QA component execution, QA component prediction, given an input question as well as the static and dynamic composition of different QA pipelines.
Author(s)
Singh, Kuldeep  
Lytra, Ioanna  
Radhakrishna, Arun Sethupat
Vyas, Akhilesh
Vidal, Maria-Esther  
Mainwork
41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018  
Conference
International Conference on Research and Development in Information Retrieval (SIGIR) 2018  
Open Access
DOI
10.1145/3209978.3210175
Additional full text version
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English
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