  Emotion cause analysis has received considerable attention in recent years.
Previous studies primarily focused on emotion cause extraction from texts in
news articles or microblogs. It is also interesting to discover emotions and
their causes in conversations. As conversation in its natural form is
multimodal, a large number of studies have been carried out on multimodal
emotion recognition in conversations, but there is still a lack of work on
multimodal emotion cause analysis. In this work, we introduce a new task named
Multimodal Emotion-Cause Pair Extraction in Conversations, aiming to jointly
extract emotions and their associated causes from conversations reflected in
multiple modalities (text, audio and video). We accordingly construct a
multimodal conversational emotion cause dataset, Emotion-Cause-in-Friends,
which contains 9,272 multimodal emotion-cause pairs annotated on 13,509
utterances in the sitcom Friends. We finally benchmark the task by establishing
a baseline system that incorporates multimodal features for emotion-cause pair
extraction. Preliminary experimental results demonstrate the potential of
multimodal information fusion for discovering both emotions and causes in
conversations.
