@inproceedings{benkler-etal-2024-recognizing,
    title = "Recognizing Value Resonance with Resonance-Tuned {R}o{BERT}a Task Definition, Experimental Validation, and Robust Modeling",
    author = "Benkler, Noam K.  and
      Friedman, Scott  and
      Schmer-Galunder, Sonja  and
      Mosaphir, Drisana Marissa  and
      Goldman, Robert P.  and
      Wheelock, Ruta  and
      Sarathy, Vasanth  and
      Kantharaju, Pavan  and
      McLure, Matthew D.",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1195",
    pages = "13688--13698",
    abstract = "Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts {--} such as long-form narratives {--} and domain-expert interviews is a fundamental goal of social anthropology. This paper builds upon a 2022 study that introduced the NLP task of Recognizing Value Resonance (RVR) for gauging perspective {--} positive, negative, or neutral {--} on implicit values and beliefs in textual pairs. This study included a novel hand-annotated dataset, the World Values Corpus (WVC), designed to simulate the task of RVR, and a transformer-based model, Resonance-Tuned RoBERTa, designed to model the task. We extend existing work by refining the task definition and releasing the World Values Corpus (WVC) dataset. We further conduct several validation experiments designed to robustly evaluate the need for task specific modeling, even in the world of LLMs. Finally, we present two additional Resonance-Tuned models trained over extended RVR datasets, designed to improve RVR model versatility and robustness. Our results demonstrate that the Resonance-Tuned models outperform top-performing Recognizing Textual Entailment (RTE) models in recognizing value resonance as well as zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. Our findings highlight the potential of RVR in capturing cultural values within texts and the importance of task-specific modeling.",
}

Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts {--} such as long-form narratives {--} and domain-expert interviews is a fundamental goal of social anthropology. This paper builds upon a 2022 study that introduced the NLP task of Recognizing Value Resonance (RVR) for gauging perspective {--} positive, negative, or neutral {--} on implicit values and beliefs in textual pairs. This study included a novel hand-annotated dataset, the World Values Corpus (WVC), designed to simulate the task of RVR, and a transformer-based model, Resonance-Tuned RoBERTa, designed to model the task. We extend existing work by refining the task definition and releasing the World Values Corpus (WVC) dataset. We further conduct several validation experiments designed to robustly evaluate the need for task specific modeling, even in the world of LLMs. Finally, we present two additional Resonance-Tuned models trained over extended RVR datasets, designed to improve RVR model versatility and robustness. Our results demonstrate that the Resonance-Tuned models outperform top-performing Recognizing Textual Entailment (RTE) models in recognizing value resonance as well as zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. Our findings highlight the potential of RVR in capturing cultural values within texts and the importance of task-specific modeling.
Benkler, Noam K. Friedman, Scott Schmer-Galunder, Sonja Mosaphir, Drisana Marissa Goldman, Robert P. Wheelock, Ruta Sarathy, Vasanth Kantharaju, Pavan McLure, Matthew D.
Recognizing Value Resonance with Resonance-Tuned RoBERTa Task Definition, Experimental Validation, and Robust Modeling