LLukas22/NLQuAD

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BibTeX:
```json
@inproceedings{soleimani-etal-2021-nlquad,
    title = "{NLQ}u{AD}: A Non-Factoid Long Question Answering Data Set",
    author = "Soleimani, Amir  and
      Monz, Christof  and
      Worring, Marcel",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-main.106",
    doi = "10.18653/v1/2021.eacl-main.106",
    pages = "1245--1255",
    abstract = "We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions. We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans. To establish baseline performances, we compare BERT, RoBERTa, and Longformer models. Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements. NLQuAD{'}s s

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dataset_licence({ "id": "LLukas22/NLQuAD" })
signal: "permissive"
stated_licence: "cc-by-3.0"
sources: 0