Modeling both Intra- and Inter-modal Influence for Real-Time Emotion Detection in Conversations

Abstract

Through much exploration in the past decade, emotion analysis in conversations was mainly conducted in textual scenario. Nowadays, with the popularization of speech and video communication, academia and industry have become gradually aware of the need in multimodal scenario. Therefore, emotion detection in conversations becomes increasingly hot not only in natural language processing (NLP) community but also in multimodal analysis community. Although previous studies normally argue that the emotion of current utterance in a conversation is much influenced by the content of historical utterances, their speakers and emotions, they model the influence derived from the history to the current utterance at the same granularity (Intra-modal influence). Intuitively, the clues of emotion detection may not exist in the history of the same modality as current utterance, but in the history of other modalities (Inter-modal influence). Besides, previous studies normally model the information propagation as the conversation flow. Intuitively, bidirectional modeling of information propagation in conversations provides rich clues for emotion detection. Therefore, this paper proposes a bidirectional dynamic dual influence network for real-time emotion detection in conversations, which can simultaneously model both intra- and inter-modal influence with bidirectional information propagation for current utterance and its historical utterances. Detailed experiments demonstrate that our approach much advances the state-of-the-art.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 35 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.