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Querying NoSQL with Deep Learning to Answer Natural Language Questions [in press]

Blank, Sebastian; Wilhelm, Florian; Zorn, Hans-Peter; Rettinger, Achim

Abstract:
Almost all of today’s knowledge is stored in databases and Thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.

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Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000092889
Erschienen in Conference on Innovative Applications of Artificial Intelligence (IAAI-19)
Verlag AAAI Press, Menlo Park (CA)
Externe Relationen Abstract/Volltext
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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