Efficient and responsible transformer based conversational agents for emotionally supportive dialogue
Year: 2026
Type: article
Abstract: Conversational agents designed for emotionally supportive interactions face challenges in balancing affective responsiveness, computational efficiency, and safety in communication. Prior approaches frequently depend on large-scale models, handcrafted affective objectives, or reinforcement learning from human feedback, which can limit scalability and interpretability. This work presents a lightweight, domain-adapted dialogue generation system based on the T5-small architecture, fine-tuned on MentalChat16K, a curated corpus of real and synthetic emotional-support conversations. The proposed model operates without reinforcement learning or emotion-specific training objectives, yet demonstrates strong alignment with affective cues and high response fluency. Empirical evaluation shows improvements over zero-shot and fine-tuned GPT-2 baselines, achieving BLEU (32.14), ROUGE-L (44.72), and BERTScore-F1 (85.11). Expert human assessments confirm high ratings in coherence, emotional appropriateness, and contextual relevance, with substantial inter-rater agreement. Qualitative error analysis indicates conservative and context-aware responses, with no hallucinations or unsafe content. The system is deployed via a browser-based Gradio interface supporting both CPU and GPU inference, featuring usage disclaimers and non-clinical positioning to ensure responsible deployment. This study demonstrates that compact transformer-based models, when adapted to domain-specific corpora and evaluated comprehensively, can enable efficient, affectively competent conversational systems suitable for large-scale, safe deployment in emotionally supportive dialogue scenarios.
Source: Discover Artificial Intelligence
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7161501466