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A question answering system on regulatory documents

: Collarana, D.; Heuss, T.; Lehmann, J.; Lytra, I.; Maheshwari, G.; Nedelchev, R.; Schmidt, T.; Trivedi, P.


Palmirani, M.:
Legal knowledge and information systems : JURIX 2018; the Thirty-first Annual Conference, Groningen, the Netherlands, December 2018
Amsterdam: IOS Press, 2018 (Frontiers in Artificial Intelligence and Applications 313)
ISBN: 978-1-61499-934-8 (Print)
ISBN: 978-1-61499-935-5 (Online)
International Conference on Legal Knowledge and Information Systems (JURIX) <31, 2018, Groningen>
Fraunhofer IAIS ()

In this work, we outline an approach for question answering over regulatory documents. In contrast to traditional means to access information in the domain, the proposed system attempts to deliver an accurate and precise answer to user queries. This is accomplished by a two-step approach which first selects relevant paragraphs given a question; and then compares the selected paragraph with user query to predict a span in the paragraph as the answer. We employ neural network based solutions for each step, and compare them with existing, and alternate baselines. We perform our evaluations with a gold-standard benchmark comprising over 600 questions on the MaRisk regulatory document. In our experiments, we observe that our proposed system outperforms other baselines.