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

AKBC - October 3, 2021

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

Yihong Chen, Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp

ICCV - October 11, 2021

Contrast and Classify: Training Robust VQA Models

Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal

Interspeech - August 30, 2021

Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency

Yangyang Shi, Varun Nagaraja, Chunyang Wu, Jay Mahadeokar, Duc Le, Rohit Prabhavalkar, Alex Xiao, Ching-Feng Yeh, Julian Chan, Christian Fuegen, Ozlem Kalinli, Michael L. Seltzer

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) - December 13, 2021

Kaizen: Continuously Improving Teacher Using Exponential Moving Average For Semi-supervised Speech Recognition

Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy