@inproceedings{zhang-etal-2025-retrieving,
    title = "Retrieving Support to Rank Answers in Open-Domain Question Answering",
    author = "Zhang, Zeyu  and
      Moschitti, Alessandro  and
      Vu, Thuy",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1778/",
    pages = "35086--35093",
    ISBN = "979-8-89176-332-6",
    abstract = "We introduce a novel Question Answering (QA) architecture that enhances answer selection by retrieving targeted supporting evidence. Unlike traditional methods, which retrieve documents or passages relevant only to a query $q$, our approach retrieves content relevant to the combined pair $(q, a)$, explicitly emphasizing the supporting relation between the query and a candidate answer $a$. By prioritizing this relational context, our model effectively identifies paragraphs that directly substantiate the correctness of $a$ with respect to $q$, leading to more accurate answer verification than standard retrieval systems. Our neural retrieval method also scales efficiently to collections containing hundreds of millions of paragraphs. Moreover, this approach can be used by large language models (LLMs) to retrieve explanatory paragraphs that ground their reasoning, enabling them to tackle more complex QA tasks with greater reliability and interpretability."
}