MMChat: Multi-Modal Chat Dataset on Social Media
This work addresses the sparsity issue in multi-modal dialogue systems for developers, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of image-grounded dialogue sparsity in multi-modal conversations by introducing MMChat, a large-scale Chinese dataset from social media, and developed a benchmark model that improved dialogue generation by adapting attention routing on image features, showing effectiveness in handling sparsity.
Incorporating multi-modal contexts in conversation is important for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large-scale Chinese multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media, in which the sparsity issue is observed. Specifically, image-initiated dialogues in common communications may deviate to some non-image-grounded topics as the conversation proceeds. To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf. We develop a benchmark model to address the sparsity issue in dialogue generation tasks by adapting the attention routing mechanism on image features. Experiments demonstrate the usefulness of incorporating image features and the effectiveness of handling the sparsity of image features.