  The need for high-quality data has been a key issue hindering the research of
dialogue tasks. Recent studies try to build datasets through manual, web
crawling, and large pre-trained models. However, man-made data is expensive and
data collected from the internet often includes generic responses, meaningless
statements, and toxic dialogues. Automatic data generation through large models
is a cost-effective method, but for open-domain multimodal dialogue tasks,
there are still three drawbacks: 1) There is currently no open-source large
model that can accept multimodal input; 2) The content generated by the model
lacks interpretability; 3) The generated data is usually difficult to quality
control and require extensive resource to collect. To alleviate the significant
human and resource expenditure in data collection, we propose a Multimodal Data
Construction Framework (MDCF). MDCF designs proper prompts to spur the
large-scale pre-trained language model to generate well-formed and satisfactory
content. Additionally, MDCF also automatically provides explanation for a given
image and its corresponding dialogue, which can provide a certain degree of
interpretability and facilitate manual follow-up quality inspection. Based on
this, we release an Explanatory Multimodal Open-Domain dialogue dataset
(EXMODD). Experiments indicate a positive correlation between the model's
ability to generate accurate understandings and high-quality responses. Our
code and data can be found at https://github.com/poplpr/EXMODD.
