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.
1 code implementation • 21 May 2023 • Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
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 • 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.