@inproceedings{kanganis-keith-2026-op,
    title = "Op-Fed: Opinion, Stance, and Monetary Policy Annotations on {FOMC} Transcripts Using Active Learning",
    author = "Kanganis, Alisa  and
      Keith, Katherine A.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.32/",
    pages = "676--699",
    ISBN = "979-8-89176-395-1",
    abstract = "The U.S.{~}Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of hundreds of millions of people. In this work, we release Op-Fed, a dataset of 1044 human-annotated sentences and their contexts from FOMC transcripts that captures monetary policy stance{---}specifically, whether an individual FOMC member expresses support for tightening or loosening policy. We faced two major technical challenges in dataset creation: imbalanced classes{---}we estimate fewer than 8{\%} of sentences express a non-neutral stance toward monetary policy{---}and inter-sentence dependence{---}65{\%} of instances require context beyond the sentence-level to annotate. To address these challenges, we developed a five-stage hierarchical schema to isolate aspects of opinion, monetary policy, and stance toward monetary policy, as well as the level of context needed. Second, we selected instances to annotate using active learning, approximately doubling the number of positive instances across all schema aspects. Using Op-Fed, we found a top-performing, closed-weight LLM achieves 0.80 zero-shot accuracy in opinion classification but only 0.61 zero-shot accuracy classifying stance toward monetary policy{---}below our human baseline of 0.89. We expect Op-Fed to be useful for future model training, confidence calibration, and as a seed dataset for future annotation efforts."
}