mlsanityData to 7 Oct 2026

Modeling Intention, Emotion and External World in Dialogue Systems

arXiv 2202.06476 ↗First posted 14 Feb 2022cs.CL cs.AIcited by 4

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Abstract (arXiv)

Intention, emotion and action are important elements in human activities. Modeling the interaction process between individuals by analyzing the relationships between these elements is a challenging task. However, previous work mainly focused on modeling intention and emotion independently, and neglected of exploring the mutual relationships between intention and emotion. In this paper, we propose a RelAtion Interaction Network (RAIN), consisting of Intention Relation Module and Emotion Relation Module, to jointly model mutual relationships and explicitly integrate historical intention information. The experiments on the dataset show that our model can take full advantage of the intention, emotion and action between individuals and achieve a remarkable improvement over BERT-style baselines. Qualitative analysis verifies the importance of the mutual interaction between the intention and emotion.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

14 of 23 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
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  9. 2015Natural Language Inference by Tree-Based Convolution and Heuristic Matchingcited by 42
  10. 2020DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classificationcited by 28
  11. 2020Guiding attention in Sequence-to-sequence models for Dialogue Act predictioncited by 26
  12. 2018Multi-task dialog act and sentiment recognition on Mastodoncited by 15
2 more
  1. 2020Bi-directional Cognitive Thinking Network for Machine Reading Comprehensioncited by 3
  2. 2021MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehensioncited by 2

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2022Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversationcited by 56
  2. 2022CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generationcited by 18
  3. 2022CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspectivecited by 2
  4. 2025Multimodal Fine-grained Context Interaction Graph Modeling for Conversational Speech Synthesiscited by 2