Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer

TL;DR

This paper introduces a novel approach combining Masked Memory Networks and Transformers to improve emotion recognition and reasoning about emotion flips in multi-party conversations, using an augmented MELD dataset.

Contribution

It proposes a new task, Emotion-Flip Reasoning, and develops models that outperform existing methods in recognizing emotions and their triggers in conversations.

Findings

Enhanced accuracy in emotion recognition on MELD dataset.

Effective identification of emotion-flip triggers.

Superiority over five state-of-the-art models.

Abstract

Efficient discovery of a speaker's emotional states in a multi-party conversation is significant to design human-like conversational agents. During a conversation, the cognitive state of a speaker often alters due to certain past utterances, which may lead to a flip in their emotional state. Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances. In this paper, along with addressing the task of emotion recognition in conversations (ERC), we introduce a novel task - Emotion-Flip Reasoning (EFR), that aims to identify past utterances which have triggered one's emotional state to flip at a certain time. We propose a masked memory network to address the former and a Transformer-based network for the latter task. To this end, we consider MELD, a benchmark emotion recognition dataset in multi-party conversations for the task of ERC, and augment it with new ground-truth labels for EFR. An extensive comparison with five state-of-the-art models suggests improved performances of our models for both tasks. We further present anecdotal evidence and both qualitative and quantitative error analyses to support the superiority of our models compared to the baselines.