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Found 6 papers, 1 papers with code
Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering
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.
Eye Gaze and Self-attention: How Humans and Transformers Attend Words in Sentences
Attention describes cognitive processes that are important to many human phenomena including reading.
Prompt-based Conservation Learning for Multi-hop Question Answering
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.
A Theory for Knowledge Transfer in Continual Learning
Continual learning of a stream of tasks is an active area in deep neural networks.
Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering
We then achieve the decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type.
Texture Modelling with Nested High-order Markov-Gibbs Random Fields
We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels.
