Multimodal Dialogue Response Generation

TL;DR

This paper introduces Divter, a novel multimodal dialogue generation model that effectively produces both textual and image responses using limited training data, outperforming existing methods in quality and informativeness.

Contribution

The paper proposes a new low-resource multimodal dialogue generation approach that isolates multimodal parameters, enabling training with limited data and achieving state-of-the-art results.

Findings

Achieves state-of-the-art performance in automatic and human evaluations.

Generates informative text and high-resolution images as responses.

Effectively learns from limited multimodal dialogue data.

Abstract

Responsing with image has been recognized as an important capability for an intelligent conversational agent. Yet existing works only focus on exploring the multimodal dialogue models which depend on retrieval-based methods, but neglecting generation methods. To fill in the gaps, we first present a multimodal dialogue generation model, which takes the dialogue history as input, then generates a textual sequence or an image as response. Learning such a model often requires multimodal dialogues containing both texts and images which are difficult to obtain. Motivated by the challenge in practice, we consider multimodal dialogue generation under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model. By this means, the major part of the model can be learned from a large number of text-only dialogues and text-image pairs respectively, then the whole parameters can be well fitted using the limited training examples. Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation, and can generate informative text and high-resolution image responses.