Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images

• URL: http://arxiv.org/abs/2107.08685v1
• Date: Mon, 19 Jul 2021 08:44:11 GMT
• Title: Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images
• Authors: Nyoungwoo Lee, Suwon Shin, Jaegul Choo, Ho-Jin Choi, Sung-Hyun Myaeng
• Abstract summary: This paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step.
• Score: 17.076424447172297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation. Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist. In response, this paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset. To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks. Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner. Our dataset and generation code is available at https://github.com/shh1574/multi-modal-dialogue-dataset.

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