EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset

• URL: http://arxiv.org/abs/2310.10967v1
• Date: Tue, 17 Oct 2023 03:28:29 GMT
• Title: EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset
• Authors: Hang Yin, Pinren Lu, Ziang Li, Bin Sun, Kan Li
• Abstract summary: We propose a Multimodal Data Construction Framework (MDCF) to alleviate the significant human and resource expenditure in data collection. MDCF automatically provides explanation for a given image and its corresponding dialogue, which can provide a certain degree of interpretability. Experiments indicate a positive correlation between the model's ability to generate accurate understandings and high-quality responses.
• Score: 20.445453185198186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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