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Learning to Select the Relevant History Turns in Conversational Question Answering
In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand.
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks
Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks.
Poisoning Attacks and Defenses in Federated Learning: A Survey
Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats.
BERT-CoQAC: BERT-based Conversational Question Answering in Context
On one hand, we introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system.
