Abstract
Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.
Highlights
With the rapid growth of Artificial Intelligence (AI), multimodal emotion recognition has become a major research topic, primarily due to its potential applications in many challenging tasks, such as dialogue generation, user behavior understanding, multimodal interaction, and others
The remainder of the paper is organized as follows: Section 2 illustrates the EmotionLines dataset; we present Multimodal EmotionLines Dataset (MELD) in Section 3; strong baselines and experiments are elaborated in Section 4; future directions and applications of MELD are covered in Section 5 and 6, respectively; Section 7 concludes the paper
We introduced MELD, a multimodal multi-party conversational emotion recognition dataset
Summary
With the rapid growth of Artificial Intelligence (AI), multimodal emotion recognition has become a major research topic, primarily due to its potential applications in many challenging tasks, such as dialogue generation, user behavior understanding, multimodal interaction, and others. A conversational emotion recognition system can be used to generate appropriate responses by analyzing user emotions (Zhou et al, 2017; Rashkin et al, 2018). Recent work proposes solutions based on multimodal memory networks (Hazarika et al, 2018). They are mostly limited to dyadic conversations, and not scalable to ERC with multiple interlocutors. This calls for a multi-party conversational data resource that can encourage research in this direction
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