On the shoulders of textual dialog systems, the multimodal ones, recently have engaged increasing attention, especially in the retail domain. Despite the commercial value of multimodal dialog systems, they still suffer from the following challenges: 1) automatically generate the right responses in appropriate medium forms; 2) jointly consider the visual cues and the side information while selecting product images; and 3) guide the response generation with multi-faceted and heterogeneous knowledge. To address the aforementioned issues, we present a Multimodal diAloG system with adaptIve deCoders, MAGIC for short.

  In particular, MAGIC first judges the response type and the corresponding medium form via understanding the intention of the given multimodal context. Hereafter, it employs adaptive decoders to generate the desired responses: a simple  recurrent neural network (RNN) is applied to generating general responses, then a knowledge-aware RNN decoder is designed to encode the multiform domain knowledge to enrich the response, and the multimodal response decoder incorporates an image recommendation model which  jointly considers the textual attributes and the visual images via a neural model optimized by the max-margin loss. We comparatively justify MAGIC over a benchmark dataset. Experiment results demonstrate that MAGIC outperforms the existing methods and achieves the state-of-the-art performance.