Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

Wang, Jiashuo; Li, Wenjie; Lin, Peiqin; Mu, Feiteng

doi:10.1016/j.knosys.2021.107547

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Also, in the context of emotion recognition in conversations, Wang et al [154] use KGs to integrate emotional causality reasoning into empathetic response generation, improving the explainability of both the emotion recognition and the utterance generation tasks. In their work, the user's emotional experience is represented by using a series of emotional causality graphs via multihop reasoning over commonsense KGs.…”

Section: Graphsmentioning

confidence: 99%

“…These graphs are then used to build an embedding and generate an empathetic response. While most existing works focus on what the emotion is and ignore how the emotion is evoked, Wang et al [154] show that the use of KGs not only helps with creating a context-aware embedding that can be exploited to synthesize an empathetic utterance but also contributes toward an improved understanding and interpretability of the system.…”

Section: Graphsmentioning

confidence: 99%

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Toward Explainable Affective Computing: A Review

Cortiñas-Lorenzo,

Lacey

2024

IEEE Trans. Neural Netw. Learning Syst.

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Affective computing has an unprecedented potential to change the way humans interact with technology. While the last decades have witnessed vast progress in the field, multimodal affective computing systems are generally black box by design. As affective systems start to be deployed in real-world scenarios, such as education or healthcare, a shift of focus toward improved transparency and interpretability is needed. In this context, how do we explain the output of affective computing models? and how to do so without limiting predictive performance? In this article, we review affective computing work from an explainable AI (XAI) perspective, collecting and synthesizing relevant papers into three major XAI approaches: premodel (applied before training), in-model (applied during training), and postmodel (applied after training). We present and discuss the most fundamental challenges in the field, namely, how to relate explanations back to multimodal and time-dependent data, how to integrate context and inductive biases into explanations using mechanisms such as attention, generative modeling, or graph-based methods, and how to capture intramodal and cross-modal interactions in post hoc explanations. While explainable affective computing is still nascent, existing methods are promising, contributing not only toward improved transparency but, in many cases, surpassing state-of-the-art results. Based on these findings, we explore directions for future research and discuss the importance of data-driven XAI and explanation goals, and explainee needs definition, as well as causability or the extent to which a given method leads to human understanding.

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Section: Graphsmentioning

confidence: 99%

Section: Graphsmentioning

confidence: 99%

Toward Explainable Affective Computing: A Review

Cortiñas-Lorenzo,

Lacey

2024

IEEE Trans. Neural Netw. Learning Syst.

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“…However, ERG does not has the explicit goal of proactively soothing the user's negative emotion. Instead, it only reactively generates responses that are consistent with the user's emotion (Lin et al, 2019;Majumder et al, 2020;Li et al, 2020a;Wang et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

Cheng¹,

Liu²,

Li³

et al. 2022

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions. Most existing research on building ES conversation systems only considers single-turn interactions with users, which is over-simplified. In comparison, multi-turn ES conversation systems can provide ES more effectively, but face several new technical challenges, including: i) how to conduct support strategy planning that could lead to the best supporting effects; ii) how to dynamically model the user's state. In this paper, we propose a novel system named MultiESC to address these issues. For strategy planning, drawing inspiration from the A* search algorithm, we propose lookahead heuristics to estimate the future user feedback after using particular strategies, which helps to select strategies that can lead to the best long-term effects. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes. Extensive experiments show that MultiESC significantly outperforms competitive baselines in both strategy planning and dialogue generation. Our codes are available at https: //github.com/lwgkzl/MultiESC.

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“…With this data, combined with the emotion-cause details, we can create a map that shows how emotions are linked to causes, helping us understand the connections better. (Wang et al, 2021, Qian et al, 2023. Our contributions are a method to create a large-scale, high ecological validity emotion dataset, validation with automatic metrics and human evaluators, and open-sourcing the dataset 1 (700K datums) for researchers.…”

Section: Introductionmentioning

confidence: 99%

EMO-KNOW: A Large Scale Dataset on Emotion-Cause

Nguyen,

Samaradivakara,

Sasikumar

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset's reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.

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Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

Cited by 10 publications

References 8 publications

Toward Explainable Affective Computing: A Review

Toward Explainable Affective Computing: A Review

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

EMO-KNOW: A Large Scale Dataset on Emotion-Cause

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