Neural Database Operator Model

West Coast NLP Summit (WeCNLP)

Abstract

Our goal is to answer queries over facts stored in a text memory. The key challenge in NeuralDBs (Thorne et al., 2020), compared to open-book NLP such as question answering (Rajpurkar et al., 2016, inter alia), is that possibly thousands of facts must be aggregated to provide a single answer, without direct supervision. The challenges represented in NeuralDBs are important for both the NLP and database communities alike: discrete reasoning over text (Dua et al., 2019), retriever-based QA (Dunn et al., 2017) and multi-hop QA (Welbl et al., 2018; Yang et al., 2018) are common components.

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