SRLCF: Learning Controllable Schwartz Value Representations from Structured Interview Dialogues

Learning value representations from interviews requires distinguishing respondents' expressed values from values introduced by interviewers and integrating evidence across turns. We introduce InterviewValues-100K, a corpus of 100,000 single- and multi-turn English interview units with machine-generated annotations for 20 Schwartz-based value categories, retained source records, and answer-side evidence. We propose SRLCF, a dual-encoder framework that combines semantic matching between dialogues and value definitions with imbalance-aware classification through label-specific gates. Gated residuals incorporate contextual answer-marker states, while fine-value and higher-order auxiliary supervision shape a dialogue-level value representation. Across three seeds, SRLCF achieves Macro-F1 of $0.510\pm 0.005$ on the development benchmark, compared with $0.483\pm 0.003$ for DeBERTa + ASL. In a retrospective fixed-model evaluation on 1,000 human-annotated MediaSum dialogues containing at least one expressed value, Macro-F1 is 0.494 versus 0.475 for a fixed-recipe baseline rerun, with the paired 95% confidence interval for the difference spanning zero. Representation analyses show that fine-value supervision improves frozen linear-probe performance without an observed gain in fused classification. Value-centroid distances also correlate with Schwartz's circular distances (Spearman $\rho =0.502$), without a circular training constraint. These findings distinguish the benefits of semantic–supervised fusion for value detection from those of fine-value supervision for representation learning.