Hugging Face's logo

Datasets:
HiTZ
/
meta4xnli

Tasks:
Token Classification
Text Classification
Modalities:
Text
Languages:
English
Spanish
Size:
10K - 100K
ArXiv:
License:
Dataset card Viewer Files Community
1
meta4xnli / README.md
elisanchez-beep's picture
elisanchez-beep
Update README.md
ddddf5a verified
preview code
|
raw
history blame
No virus
2.53 kB
metadata
license: apache-2.0
task_categories:
  - token-classification
  - text-classification
language:
  - en
  - es
pretty_name: meta4xnli
size_categories:
  - 1K<n<10K

Dataset Card for Dataset Name

Meta4XNLI is a parallel dataset with annotations in English and Spanish for metaphor detection at token level (13320 sentences) and metaphor interpretation framed within NLI the task (9990 premise-hypothesis pairs). It is a collection of existing NLI datasets manually labeled for both metaphor tasks.

Dataset Sources

Meta4XNLI is a collection of XNLI and esXNLI datasets with metaphor annotations.

Dataset Structure

The dataset is divided according to detection and interpretation tasks.

  • Detection: labels at token level.
    • splits: train, dev and test files for fine-tuning and evaluation.
    • source_datasets: splits by original source dataset and premises and hypotheses for evaluation.
  • Intepretation: set of sentences split by metaphor occurrence. Non-relevant cases include sentences with metaphors, however, their literal interpretation is not necessary to extract the inference label.
    • splits: train, dev and test files for fine-tuning and evaluation.
    • source_datasets: splits by original source dataset and metaphor presence.

Dataset Fields

  • Detection:

    • "id": example id
    • "tokens": list of text split.
    • "tags": list of metaphor annotations for each token.
      • 0: literal
      • 1: metaphor

  • Interpretation:

    • "language": Spanish (es) or English (en)
    • "gold_label": inference label: entailment, neutral or contradiction
    • "sentence1": premise
    • "sentence2": hypothesis
    • "promptID": premise id
    • "pairID": premise and hypothesis pair id
    • "genre": text domain
    • "source_dataset": original dataset: {xnli.dev, xnli.test, esxnli}

Citation [optional]

If you use Meta4XNLI, please cite our work:

@misc{sanchezbayona2024meta4xnli,
      title={Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation}, 
      author={Elisa Sanchez-Bayona and Rodrigo Agerri},
      year={2024},
      eprint={2404.07053},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Dataset Card Contact

{elisa.sanchez, rodrigo.agerri}@ehu.eus