no code implementations • CMCL (ACL) 2022 • Joshua Bensemann, Alex Peng, Diana Prado, Yang Chen, Neset Tan, Paul Michael Corballis, Patricia Riddle, Michael Witbrock
Attention describes cognitive processes that are important to many human phenomena including reading.
1 code implementation • NAACL (DLG4NLP) 2022 • Zhenyun Deng, Yonghua Zhu, Qianqian Qi, Michael Witbrock, Patricia Riddle
Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed through the graph in hops.
no code implementations • COLING 2022 • Zhenyun Deng, Yonghua Zhu, Yang Chen, Qianqian Qi, Michael Witbrock, Patricia Riddle
In this paper, we propose the Prompt-based Conservation Learning (PCL) framework for multi-hop QA, which acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop QA tasks, mitigating forgetting.
no code implementations • 14 Aug 2022 • Diana Benavides-Prado, Patricia Riddle
Continual learning of a stream of tasks is an active area in deep neural networks.
no code implementations • 16 Jun 2022 • Zhenyun Deng, Yonghua Zhu, Yang Chen, Michael Witbrock, Patricia Riddle
We then achieve the decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type.
no code implementations • 8 Oct 2015 • Ralph Versteegen, Georgy Gimel'farb, Patricia Riddle
We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels.