I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System

2026-06-10

AI Summary

This study addresses the limited efficacy of existing dialogue systems in providing deep emotional support due to insufficient validation of user emotions. To bridge this gap, the authors introduce MEGUMI, the first systematic multilingual emotion validation framework, accompanied by the release of the M-EDESConv corpus and the M-TESC benchmark dataset. Built upon frozen XLM-RoBERTa semantic representations, MEGUMI integrates language-specific emotion encoders and employs cross-modal attention with gating mechanisms to jointly identify validating responses, detect optimal timing for validation, and generate empathetic replies. Experimental results demonstrate that MEGUMI significantly outperforms baseline models across multiple languages, achieving strong performance on both automatic metrics and human evaluations. The findings also highlight persistent limitations in current large language models’ capacity for nuanced emotion understanding.

Abstract

Emotional validation - explicitly acknowledging that a user's feelings make sense - has proven therapeutic value but has received little computational attention. Emotional validation in dialogue systems can be decomposed into (i) validating response identification, (ii) validation timing detection, and (iii) validating response generation. To support research on all three subtasks, we release M-EDESConv, a 120k English-Japanese multilingual corpus created through hybrid manual and automatic annotation, and M-TESC, a multilingual spoken-dialogue test set. For timing detection, we propose MEGUMI, a Multilingual Emotion-aware Gated Unit for Mutual Integration, that fuses frozen XLM-RoBERTa semantics with language-specific emotion encoders via cross-modal attention and gated fusion. MEGUMI shows superior performance on both the M-EDESConv and M-TESC datasets, both objectively and subjectively. Finally, our EmoValidBench benchmarks of GPT-4.1 Nano and Llama-3.1 8B indicate that current LLMs generate contextually similar and diverse validating responses, but emotional understanding remains a major area for improvement. Project page: https://github.com/zihaurpang/Multilingual-Emotional-Validation

Problem

• emotional validation
• dialogue system
• multilingual
• emotion understanding
• empathetic response

Innovation

• emotional validation
• multilingual dialogue system
• MEGUMI
• XLM-RoBERTa
• emotion-aware fusion