Friends-MMC：多模多方会话数据集理解

Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding

December 23, 2024

作者: Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang, Qun Liu, Dongyan Zhao

cs.AI

摘要

多模态多方对话（MMC）是一个研究较少但重要的课题，因为它很好地适应了现实场景，因此潜在地具有更广泛的应用。与传统的多模态对话相比，MMC 需要更强的以角色为中心的理解能力，因为在视觉和文本环境中都会出现许多对话者。为了促进对这一问题的研究，本文提出了Friends-MMC，这是一个包含24,000多个独特话语与视频内容配对的MMC数据集。为了探索对话的以角色为中心的理解，我们还注释了每个话语的发言者、视频中出现的面孔的姓名和边界框。基于这个Friends-MMC数据集，我们进一步研究了两个基本的MMC任务：对话发言者识别和对话回复预测，这两个任务都具有多方性质，视频或图像作为视觉上下文。对于对话发言者识别，我们展示了现有方法（如预训练模型）的低效性，并提出了一种简单而有效的基线方法，利用优化求解器来利用两种模态的上下文以实现更好的性能。对于对话回复预测，我们在Friends-MMC上微调生成式对话模型，并分析了发言者信息的好处。代码和数据集可以在 https://github.com/yellow-binary-tree/Friends-MMC 公开获取，因此我们呼吁更多关注在理解对话时建模发言者信息。

English

Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used applications. Compared with the traditional multi-modal conversations, MMC requires stronger character-centered understanding abilities as there are many interlocutors appearing in both the visual and textual context. To facilitate the study of this problem, we present Friends-MMC in this paper, an MMC dataset that contains 24,000+ unique utterances paired with video context. To explore the character-centered understanding of the dialogue, we also annotate the speaker of each utterance, the names and bounding bboxes of faces that appear in the video. Based on this Friends-MMC dataset, we further study two fundamental MMC tasks: conversation speaker identification and conversation response prediction, both of which have the multi-party nature with the video or image as visual context. For conversation speaker identification, we demonstrate the inefficiencies of existing methods such as pre-trained models, and propose a simple yet effective baseline method that leverages an optimization solver to utilize the context of two modalities to achieve better performance. For conversation response prediction, we fine-tune generative dialogue models on Friend-MMC, and analyze the benefits of speaker information. The code and dataset is publicly available at https://github.com/yellow-binary-tree/Friends-MMC and thus we call for more attention on modeling speaker information when understanding conversations.