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sapienzanlp/LiteraryQA

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Permissive or share-alike

The declared licence (cc-by-sa-4.0) is classified as share-alike.

Declared licence: cc-by-sa-4.0, from the tagEvidence: README.md at b67aa75
Licence files: No licence file at the top level of the repositoryEvidence: file list at b67aa75
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

question-answering

Citation text from the card

@inproceedings{bonomo-etal-2025-literaryqa,
    title = "{L}iterary{QA}: Towards Effective Evaluation of Long-document Narrative {QA}",
    author = "Bonomo, Tommaso  and
      Gioffr{\'e}, Luca  and
      Navigli, Roberto",
    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://aclanthology.org/2025.emnlp-main.1729/",
    doi = "10.18653/v1/2025.emnlp-main.1729",
    pages = "34074--34095",
    ISBN = "979-8-89176-332-6",
    abstract = "Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents. However, the reliability of NarrativeQA, the most widely used benchmark in this domain, is hindered by noisy documents and flawed QA pairs. In this work, we introduce LiteraryQA, a high-quality subset of NarrativeQA focused on literary works. Using a human- and LLM-validated pipeline, we identify and correct low-quality QA samples while removing extraneous text from source documents. We then carry out a meta-evaluation of automatic metrics to clarify how systems should be evaluated on LiteraryQA.This analysis reveals that all n-gram-based metrics have a low system-level correlation

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dataset_licence({ "id": "sapienzanlp/LiteraryQA" })
signal: "permissive"
stated_licence: "cc-by-sa-4.0"
sources: 0