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STICKERCONV: Generating Multimodal Empathetic Responses from Scratch

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arxiv 2402.01679 v2 pith:PK65XS24 submitted 2024-01-20 cs.CL cs.AI

Yiqun Zhang , Fanheng Kong , Peidong Wang , Shuang Sun , Lingshuai Wang , Shi Feng , Daling Wang , Yifei Zhang
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Kaisong Song
This is my paper · ORCID
classification cs.CLcs.AI
keywords empatheticmultimodaldialoguestickerconvstickersagentcommunicationcomprehensive
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Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets. In this paper, we introduce the Agent for STICKERCONV (Agent4SC), which uses collaborative agent interactions to realistically simulate human behavior with sticker usage, thereby enhancing multimodal empathetic communication. Building on this foundation, we develop a multimodal empathetic dialogue dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. This dataset serves as a benchmark for multimodal empathetic generation. To advance further, we propose PErceive and Generate Stickers (PEGS), a multimodal empathetic response generation framework, complemented by a comprehensive set of empathy evaluation metrics based on LLM. Our experiments demonstrate PEGS's effectiveness in generating contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging empathetic dialogue systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluation and Hardening of LLM System Instructions Against Extraction via Encoding Attacks

    cs.CR 2026-04 conditional novelty 5.0 of 10

    Refusal-aligned LLMs leak system instructions under encoding/serialization prompts at high rates, and one-shot CoT instruction reshaping substantially reduces that leakage without retraining.

  2. MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A sticker emotion recognizer that uses an LLM's multi-view text descriptions to guide a pyramid vision transformer beats previous methods on SER30K and MET-MEME.

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