39 citations · 106 across the 12 of their papers we have counts for
5 papers · 2 filters
Paraphrasing with Large Language Models
Sam Witteveen, Martin Andrews
Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstr…
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
Yew Ken Chia, Sam Witteveen, Martin Andrews
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's e…
Unsupervised Natural Question Answering with a Small Model
Martin Andrews, Sam Witteveen
The recent (2019-02) 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 knowle…
Scene Graph Parsing by Attention Graph
Martin Andrews, Yew Ken Chia, Sam Witteveen
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language appl…
Relationships from Entity Stream
Martin Andrews, Sam Witteveen
Relational reasoning is a central component of intelligent behavior, but has proven difficult for neural networks to learn. The Relation Network (RN) module was recently proposed b…