RESEARCH

NLP

Neural Database Operator Model

October 21, 2020

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.

Download the Paper

AUTHORS

Written by

James Thorne

Alon Halevy

Majid Yazdani

Marzieh Saeidi

Sebastian Riedel

Publisher

WeCNLP

Related Publications

December 15, 2021

RESEARCH

Sample-and-threshold differential privacy: Histograms and applications

Akash Bharadwaj, Graham Cormode

December 15, 2021

December 06, 2021

NLP

Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling

Hongyu Gong, Yun Tang, Juan Miguel Pino, Xian Li

December 06, 2021

November 16, 2021

NLP

Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN

Rahma Chaabouni, Roberto Dessì, Evgeny Kharitonov

November 16, 2021

November 08, 2021

NLP

CORE MACHINE LEARNING

DOBF: A Deobfuscation Pre-Training Objective for Programming Languages

Baptiste Rozière, Marie-Anne Lachaux, Marc Szafraniec, Guillaume Lample

November 08, 2021