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Abstract Topic models have been thoroughly investigated for multiple years due to their great potential in analyzing and understanding texts. Recently, researchers combine the study of topic models with deep learning techniques, known as Neural Topic Models (NTMs).

This paper proposes a causal inference based method for debiased, longitudinal and coordinated drug recommendation.

Abstract Stylized dialogue generation, which aims to generate a given-style response for an input context, plays a vital role in intelligent dialogue systems. Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.