A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems

TL;DR

This paper introduces a non-hierarchical attention network with modality dropout for multimodal dialogue systems, improving interaction modeling and context representation over traditional hierarchical methods, achieving state-of-the-art results.

Contribution

The paper proposes a novel non-hierarchical attention-based model with modality dropout that enhances feature interaction and context encoding in multimodal dialogue systems.

Findings

Outperforms existing methods on a public dataset

Achieves state-of-the-art performance in automatic evaluation

Receives positive human evaluation feedback

Abstract

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.