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To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations.
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.
Fact-based dialogue generation is a task of generating a human-like response based on both dialogue context and factual texts.
Most generation models are based on language models.
Neural conversation models have shown the power to produce more meaningful and engaging responses given external knowledge.
Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation.
Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.
To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.
We describe Panacea, a system that supports natural language processing (NLP) components for active defenses against social engineering attacks.