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Found 5 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.
Improving information retrieval through correspondence analysis instead of latent semantic analysis
The elements of the raw document-term matrix are weighted, and the weighting exponent of singular values is adjusted to improve the performance of LSA.
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.
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions
We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform.
A comparison of latent semantic analysis and correspondence analysis of document-term matrices
In this article, we present a theoretical analysis and comparison of the two techniques in the context of document-term matrices.
