Emotional Support Conversation (ESC) aims to alleviate users’ emotional distress through multi-turn interactions by selecting appropriate support strategies and generating aligned responses. Despite recent progress, existing approaches face three key limitations: (1) the inability to capture sequential transition patterns of strategies within conversations; (2) a training-inference mismatch in strategy prediction; and (3) reliance on a shared decoder that limits strategy-specific expression. To address these challenges, we propose S3-ESC, a simple framework of three components that are model-agnostic across encoder-decoder ESC backbones. First, we introduce a Sequential strategy predictor that models strategy transition patterns. Second, we apply Scheduled sampling for strategy to mitigate the training-inference mismatch. Third, we propose Selective LoRA for strategy that enables strategy-specialized generation. Experiments on six ESC backbones show that S3-ESC consistently improves strategy prediction accuracy, by up to 4.5pp, while enhancing response quality on most of them. Our code is publicly available.

We release large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis. We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles. We also provide baseline performances for future work by evaluating recent models on the released datasets.