As a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to
explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In …

In human-computer interaction, it is crucial for agents to respond to human by understanding
their emotions. Unraveling the causes of emotions is more challenging. A new task named …

Core to much of modern deep learning is the notion of representation learning, learning
representations of things that are useful for performing some task (s) related to those things …

Emotion recognition in conversations (ERC) is challenging due to the multimodal nature of
the emotion expression. In this paper, we propose to pretrain a text-based recognition model …

Dialogue relation extraction identifies semantic relations between entity pairs in dialogues.
This research explores a methodology harnessing the potential of prompt-based fine-tuning …

Television is often seen as a site for subcultural identification and subversive fantasy,
including in queer cultures. How might we measure subversion, or the degree to which the …

Dialogue relation extraction (DRE) aims to extract relations between two arguments within a
dialogue, which is more challenging than standard RE due to the higher person pronoun …

Emotion Recognition in Conversations (ERC) plays a crucial role in the development of
human-machine interaction. Conversations are a multi-party, multi-emotion, and multi-turn …

With strong expressive capabilities in Large Language Models (LLMs), generative models
effectively capture sentiment structures and deep semantics, however, challenges remain in …

Abstract Speech Emotion Recognition (SER) holds a significant position in the fields of
Natural Language Processing (NLP) and Affective Computing. Traditional unimodal …