  Existing text- and image-based multimodal dialogue systems use the
traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has
an utterance-level encoder to model utterance representation and a
context-level encoder to model context representation. Although pioneer efforts
have shown promising performances, they still suffer from the following
challenges: (1) the interaction between textual features and visual features is
not fine-grained enough. (2) the context representation can not provide a
complete representation for the context. To address the issues mentioned above,
we propose a non-hierarchical attention network with modality dropout, which
abandons the HRED framework and utilizes attention modules to encode each
utterance and model the context representation. To evaluate our proposed model,
we conduct comprehensive experiments on a public multimodal dialogue dataset.
Automatic and human evaluation demonstrate that our proposed model outperforms
the existing methods and achieves state-of-the-art performance.
