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natural-language-processing-2

Causal Emotion Entailment

3260 papers • 126 benchmarks • 313 datasets

The Causal Emotion Entailment is a simpler version of the span extraction task. In this task, given a target utterance (U) with emotion E, the goal is to predict which particular utterances in the conversation history H(U) are responsible for the emotion E in the target utterance.

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Most implemented papers

Recognizing Emotion Cause in Conversations

Devamanyu Hazarika, Soujanya Poria, Rishabh Bhardwaj, Navonil Majumder, A. Gelbukh, Deepanway Ghosal, Samson Yu, Pengfei Hong, Romila Ghosh, Niyati Chhaya, Rada Mihalcea•Mon Dec 21 2020

This work introduces a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provides a new highly challenging publicly available dialogue-level dataset for this task, and gives strong baseline results on this dataset.

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Towards Interpretable Mental Health Analysis with Large Language Models

Sophia Ananiadou, Zi-Zhou Kuang, Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie•Wed Apr 05 2023

ChatGPT shows strong in-context learning ability but still has a significant gap with advanced task-specific methods, and generates explanations that approach human performance, showing its great potential in explainable mental health analysis.

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TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Fandong Meng, Jie Zhou, Xiuyi Chen, Duzhen Zhang, Zhenfei Yang•Tue Mar 01 2022

A Two-Stream Attention Model (TSAM) is proposed to effectively model the speaker’s emotional influences in the conversational history and achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably.

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Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

Ashutosh Modi, A. Bhat•Sun Nov 06 2022

This work proposes MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations, which performs better than the baselines for most of the data folds provided in the dataset.

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Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge

Fandong Meng, Jie Zhou, R. Liu, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, JiangNan Li•Sun May 01 2022

This work builds conversations as graphs to overcome implicit contextual modelling of the original entailment style and proposes a sentiment-realized knowledge selecting strategy to filter CSK.

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Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

Yanyan Zhao, Bing Qin, Weixiang Zhao, Zhuojun Li•Mon Dec 05 2022

This work proposes Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges to capture the deep inter-utterance dependencies in the conversational context via the CSK-Enhanced Graph Attention module.

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PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation

Xiaojie Gu, Renze Lou, Lin Sun, Shangxin Li•Thu Mar 02 2023

A novel position-aware graph is devised to encode the entire conversation, fully modeling causal relations among utterances, and consistently achieves state-of-the-art performance on two challenging test sets, proving the effectiveness of the model.

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Causal Emotion Entailment | State-of-the-Art