Papers › Improving Context Modelling in Multimodal Dialogue Generation
Improving Context Modelling in Multimodal Dialogue Generation
Shubham Agarwal, Ondrej Dusek, Ioannis Konstas, Verena Rieser
In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system. Our work is based on the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion domain. We introduce a multimodal extension to the Hierarchical Recurrent Encoder-Decoder (HRED) model and show that this extension outperforms strong baselines in terms of text-based similarity metrics. We also showcase the shortcomings of current vision and language models by performing an error analysis on our system's output.
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