DialogCC: A High-Quality and Diverse Multi-Modal Dialogue Dataset Created through Automated Pipelines

Core Concepts

This paper proposes an automated pipeline to construct a high-quality and diverse multi-modal dialogue dataset, DialogCC, which surpasses existing datasets in terms of quality, diversity, and generalization performance.

Abstract

The authors propose an automated pipeline to construct a high-quality and diverse multi-modal dialogue dataset, DialogCC. The pipeline involves three main steps:

Collecting source datasets: The authors collect five text-only social dialogue datasets and the Conceptual Captions 3M image-caption dataset as the source data.

Aligning images and dialogues: To ensure coherence between images and dialogue, the authors use GPT-4 to infer potential image-sharing moments, including the utterance, speaker, rationale, and image description. They then leverage CLIP similarity to maintain consistency between aligned multiple images and the utterance.

Filtering multi-modal dialogue: The authors remove inappropriate images based on CLIP similarity for image-image consistency and discard frequently matched images to avoid model overfitting.

The resulting DialogCC dataset contains high-quality and diverse multi-modal dialogues, with an average of 7.34 images per dialogue and 4.77 images per image-sharing turn. Comprehensive experiments demonstrate that DialogCC can boost the generalization performance of trained models on unseen dialogue scenarios, outperforming existing datasets like MMDD, PhotoChat, and MMDialog.

Stats

"As sharing images in an instant message is a crucial factor, there has been active research on learning an image-text multi-modal dialogue models."
"DialogCC includes the largest number of Avg. I./D. and I./U. than others. I./D. and I./U. denote images by dialogue and images by an utterance, respectively."
"DialogCC achieves better statistics compared to the existing datasets in terms of quality, diversity, and generalization."

Quotes

"We propose a fully automatic pipeline to create a multi-modal dialogue dataset that can achieve quality and diversity without human intervention."
"Extensive experiments demonstrate the effectiveness of our dataset, which enhances the generalization performance."
"The model trained on DialogCC outperforms those trained on other datasets, indicating that DialogCC significantly improves the model's comprehension of the interaction between dialogue and images."

Key Insights Distilled From

DialogCC

by Young-Jun Le... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2212.04119.pdf

Deeper Inquiries

How can the automated pipeline be further improved to ensure factual accuracy in the image-dialogue alignment process?

To enhance factual accuracy in the image-dialogue alignment process within the automated pipeline, several improvements can be implemented. Firstly, incorporating fact-checking mechanisms or external knowledge bases can help verify the accuracy of the information presented in both the dialogue and the aligned images. By cross-referencing the content with reliable sources, the pipeline can ensure that the images selected are contextually relevant and factually correct. Additionally, implementing a feedback loop where human annotators can validate the alignment of images with dialogue can further enhance the accuracy of the dataset. This iterative process of validation and refinement can help eliminate inaccuracies and improve the overall quality of the dataset.

What are the potential biases that may exist in the DialogCC dataset, and how can they be mitigated to create a more inclusive and equitable multi-modal dialogue system?

Potential biases that may exist in the DialogCC dataset include gender bias, racial bias, and cultural bias in image selection and dialogue alignment. To mitigate these biases and create a more inclusive and equitable multi-modal dialogue system, several strategies can be employed. Firstly, implementing diversity and representation guidelines during the dataset creation process can help ensure that images and dialogues reflect a wide range of perspectives and identities. Additionally, conducting bias audits and sensitivity analyses on the dataset can help identify and address any existing biases. Incorporating diverse perspectives and voices in the dataset creation process can also help mitigate biases and promote inclusivity. Finally, ongoing monitoring and evaluation of the dataset for bias detection and mitigation can ensure that the multi-modal dialogue system remains fair and unbiased.

How can the personalization aspect be incorporated into the DialogCC dataset and the corresponding multi-modal dialogue models to enhance user engagement and experience?

To incorporate the personalization aspect into the DialogCC dataset and enhance user engagement and experience, several strategies can be implemented. Firstly, capturing user preferences and interests through interactive interfaces or user feedback mechanisms can help tailor the dialogue content and image selection to individual users. This personalized approach can enhance user engagement by providing relevant and meaningful interactions. Additionally, leveraging user data and behavior patterns to dynamically adjust the dialogue content and image recommendations can create a more personalized and engaging experience. Implementing adaptive algorithms that learn from user interactions and feedback can further enhance the personalization aspect of the multi-modal dialogue system, leading to a more immersive and user-centric experience.