---
license: apache-2.0
task_categories:
- token-classification
- text-classification
language:
- en
- es
pretty_name: meta4xnli
size_categories:
- 1K<n<10K
configs:
 - config_name: det_es_finetune
   data_files:
    - split: train
      path: detection/splits/es/meta4xnli_train.jsonl
    - split: dev
      path: detection/splits/es/meta4xnli_dev.jsonl
    - split: test
      path: detection/splits/es/meta4xnli_test.jsonl
 - config_name: det_en_finetune
   data_files:
    - split: train
      path: detection/splits/en/meta4xnli_train.jsonl
    - split: dev
      path: detection/splits/en/meta4xnli_dev.jsonl
    - split: test
      path: detection/splits/en/meta4xnli_test.jsonl
 - config_name: det_es_eval
   data_files:
    - split: esxnli_prem
      path: detection/source_datasets/es/esxnli_prem.jsonl
    - split: esxnli_hyp
      path: detection/source_datasets/es/esxnli_hyp.jsonl
    - split: xnli_dev_prem
      path: detection/source_datasets/es/xnli_dev_prem.jsonl
    - split: xnli_dev_hyp
      path: detection/source_datasets/es/xnli_dev_hyp.jsonl
    - split: xnli_test_prem
      path: detection/source_datasets/es/xnli_test_prem.jsonl
    - split: xnli_test_hyp
      path: detection/source_datasets/es/xnli_test_hyp.jsonl
 - config_name: det_en_eval
   data_files:
    - split: esxnli_prem
      path: detection/source_datasets/en/esxnli_prem.jsonl
    - split: esxnli_hyp
      path: detection/source_datasets/en/esxnli_hyp.jsonl
    - split: xnli_dev_prem
      path: detection/source_datasets/en/xnli_dev_prem.jsonl
    - split: xnli_dev_hyp
      path: detection/source_datasets/en/xnli_dev_hyp.jsonl
    - split: xnli_test_prem
      path: detection/source_datasets/en/xnli_test_prem.jsonl
    - split: xnli_test_hyp
      path: detection/source_datasets/en/xnli_test_hyp.jsonl
 - config_name: int_finetune
   datafiles:
    - split: train_no_met
      path : interpretation/splits/train_no_met.jsonl
    - split: train_met
      path: interpretation/splits/train_met.jsonl
    - split: train_nonrelevant
      path: interpretation/splits/train_nonrelevant.jsonl
    - split: dev_no_met
      path: interpretation/splits/dev_no_met.jsonl
    - split: dev_met
      path: interpretation/splits/dev_met.jsonl
    - split: dev_nonrelevant
      path: interpretation/splits/dev_nonrelevant.jsonl
    - split: test_no_met
      path: interpretation/splits/test_no_met.jsonl
    - split: test_met
      path: interpretation/splits/test_met.jsonl
    - split: test_nonrelevant
      path: interpretation/splits/test_nonrelevant.jsonl
 - config_name: int_eval
   datafiles:
    - split: esxnli_met
      path : interpretation/source_datasets/esxnli_met.jsonl
    - split: esxnli_no_met
      path : interpretation/source_datasets/esxnli_no_met.jsonl
    - split: esxnli_nonrelevant
      path : interpretation/source_datasets/esxnli_nonrelevant.jsonl
    - split: xnli_dev_met
      path : interpretation/source_datasets/xnli_dev_met.jsonl
    - split: xnli_dev_no_met
      path : interpretation/source_datasets/xnli_dev_no_met.jsonl
    - split: xnli_dev_nonrelevant
      path : interpretation/source_datasets/xnli_dev_nonrelevant.jsonl
    - split: xnli_test_met
      path : interpretation/source_datasets/xnli_test_met.jsonl
    - split: xnli_test_no_met
      path : interpretation/source_datasets/xnli_test_no_met.jsonl
    - split: xnli_test_nonrelevant
      path : interpretation/source_datasets/xnli_test_nonrelevant.jsonl
    
---

# 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.

- **Repository**: data available also in .tsv format at https://github.com/elisanchez-beep/meta4xnli
- **Paper**: [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://arxiv.org/pdf/2404.07053.pdf)


### Dataset Sources

Meta4XNLI is a collection of [XNLI](xnli) and [esXNLI](https://aclanthology.org/2020.emnlp-main.618/) 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