Abstract:Medical dialogue generation is an important yet challenging task. Most previous works rely on the attention mechanism and largescale pretrained language models. However, these methods often fail to acquire pivotal information from the long dialogue history to yield an accurate and informative response, due to the fact that the medical entities usually scatters throughout multiple utterances along with the complex relationships between them. To mitigate this problem, we propose a medical response generation mod… Show more
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