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In human conversation an input post is open to multiple potential responses,which is typically regarded as a one-to-many problem. Promising approachesmainly incorporate multiple latent mechanisms to build the one-to-manyrelationship. However, without accurate selection of the latent mechanismcorresponding to the target response during training, these methods suffer froma rough optimization of latent mechanisms. In this paper, we propose amulti-mapping mechanism to better capture the one-to-many relationship, wheremultiple mapping modules are employed as latent mechanisms to model thesemantic mappings from an input post to its diverse responses. For accurateoptimization of latent mechanisms, a posterior mapping selection module isdesigned to select the corresponding mapping module according to the targetresponse for further optimization. We also introduce an auxiliary matching lossto facilitate the optimization of posterior mapping selection. Empiricalresults demonstrate the superiority of our model in generating multiple diverseand informative responses over the state-of-the-art methods.

Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection