Year: 2019
Type: conference-paper
Abstract: The recent demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models.This short paper describes an architecture through which much smaller models can also answer such questions -by making use of 'raw' external knowledge.The contribution of this work is that the methods presented here rely on unsupervised learning techniques, complementing the unsupervised training of the Language Model.The goal of this line of research is to be able to add knowledge explicitly, without extensive training.
DOI: 10.18653/v1/d19-6606
Authors: Martin Andrews, Sam Witteveen
Institution: AI Singapore
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2990170844