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From natural language processing to neural databases

Thorne, J; Yazdani, M; Saeidi, M; Silvestri, F; Riedel, S; Halevy, A; (2021) From natural language processing to neural databases. In: PVLDB – Proceedings of the VLDB Endowment. (pp. pp. 1033-1039). VLDB Endowment: Copenhagen, Denmark. Green open access

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In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases, a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy.

Type: Proceedings paper
Title: From natural language processing to neural databases
Event: 47th International Conference on Very Large Data Bases 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.14778/3447689.3447706
Publisher version: https://dl.acm.org/journal/pvldb
Language: English
Additional information: This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-ncnd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10125401
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