Neural Database Operator Model

West Coast NLP Summit (WeCNLP)


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.

Related Publications

All Publications

arxiv - November 1, 2020

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine

IJCNN - July 18, 2021

Power Pooling: An Adaptive Pooling Function for Weakly Labelled Sound Event Detection

Yuzhuo Liu, Hangting Chen, Yun Wang, Pengyuan Zhang

ACL - August 1, 2021

Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?

Pedro Rodriguez, Joe Barrow, Alexander Hoyle, John P. Lalor, Robin Jia, Jordan Boyd-Graber

FAST - February 23, 2021

Evolution of Development Priorities in Key-value Stores Serving Large-scale Applications: The RocksDB Experience

Siying Dong, Andrew Kryczka, Yanqin Jin, Michael Stumm

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