Demo Paper accepted at SIGIR 2018
We are very pleased to announce that our group got 1 papers accepted for presentation at the demo session on SIGIR 2018: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, which will be held on Ann Arbor Michigan, U.S.A. July 8-12, 2018.The annual SIGIR conference is the major international forum for the presentation of new research results, and the demonstration of new systems and techniques, in the broad field of information retrieval (IR). The 41st ACM SIGIR conference welcomes contributions related to any aspect of information retrieval and access, including theories and foundations, algorithms and applications, and evaluation and analysis. The conference and program chairs invite those working in areas related to IR to submit high-impact original papers for review. Here is the accepted paper with its abstract:
- “Dynamic Composition of Question Answering Pipelines with Frankenstein” by Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Akhilesh Vyas and Maria Esther Vidal.
Abstract: Question answering (QA) systems provide user-friendly interfaces for retrieving answers from structured and unstructured data to 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 to generate static and dynamic executable QA pipelines. In this demo, attendees will be able to view the 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.AcknowledgmentThis work has received funding from the EU H2020 R&I programme for the Marie Skłodowska-Curie action WDAqua (GA No 642795.
Looking forward to seeing you at The SIGR 2018.Tweet