Combining Emotion Perception and Keywords to Support Emotional Dialogue Research
Recent advances in artificial intelligence have enabled the development of more natural and effective human-machine interactions. In this study, we propose an enhanced BART-based dialogue generation framework that integrates emotion labels and keyword annotations to support emotionally aware end-to-end conversations. The proposed system combines an independent RoBERTa-based emotion classification module with a terminology-aware annotation mechanism, in which emotional labels and key semantic terms are explicitly injected into the dialogue context. The framework is evaluated on a merged dataset constructed from two benchmark dialogue corpora, Empathetic Dialogues and DailyDialog, which have been relabeled into nine unified emotion categories. Automatic evaluation is conducted using BLEU, ROUGE, and perplexity metrics. Experimental results demonstrate that the proposed approach consistently outperforms baseline models, including GPT-2, DialoGPT, T5, UniLM, and the original BART model. In particular, the incorporation of keyword annotations significantly improves response relevance and informational completeness, while emotion labels further enhance emotional alignment. These findings suggest that explicitly modeling emotional cues and semantic focus can effectively improve the quality of emotional dialogue generation, offering a promising direction for the development of emotionally intelligent conversational agents.