---
license: cc-by-nc-4.0
language:
- eu
pretty_name: XNLI EU
size_categories:
- 1K<n<10K
dataset_info:
- config_name: eu
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
- config_name: eu_mt
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
- config_name: eu_native
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
configs:
- config_name: eu
  data_files:
  - split: train
    path: xnli.train.eu.mt.tsv
  - split: validation
    path: xnli.dev.eu.tsv
  - split: test
    path: xnli.test.eu.tsv
- config_name: eu_mt
  data_files:
  - split: train
    path: xnli.train.eu.mt.tsv
  - split: validation
    path: xnli.dev.eu.mt.tsv
  - split: test
    path: xnli.test.eu.mt.tsv
- config_name: eu_native
  data_files:
  - split: test
    path: xnli.test.eu.native.tsv
task_categories:
- text-classification
---
# Dataset Card for XNLIeu

<!-- Provide a quick summary of the dataset. -->

XNLIeu is an extension of [XNLI](https://huggingface.co/datasets/xnli) translated from English to **Basque**. It has been designed as a cross-lingual dataset for the Natural Language Inference task, a text-classification task that consists on classifying pairs of sentences, a premise and a hypothesis, according to their semantic relation out of three possible labels: entailment, contradiction and neutral.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. 
We expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. 
The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step.

- **Language(s) (NLP):** Basque (eu)
- **License:** XNLIeu is derived from XNLI and distributed under its same license. 

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [Link to the GitHub Repository](https://github.com/hitz-zentroa/xnli-eu/)
- **Paper:** [Link to the Paper](https://aclanthology.org/2024.naacl-long.234/)

## Uses

XNLieu is meant as an cross-lingual evaluation dataset. It can be used in combination with the train sets of [XNLI](https://huggingface.co/datasets/xnli) for a cross-lingual zero-shot setting, and we provide a machine-translated train set in both "eu" and "eu_mt" splits to implement a translate-train setting.

## Dataset Structure
The dataset has three subsets:
- **eu**: XNLIeu, machine-translated and post-edited from English to Basque.
- **eu_MT**: XNLIeu<sub>MT</sub>, a machine-translated version prior post-edition.
- **eu_native**: An original, non-translated test set.


### Splits

|    name     |train |validation|test|
|-------------|-----:|---------:|---:|
|eu           |392702|      2490|5010|
|eu_mt        |392702|      2490|5010|
|eu_native    |-     |      -   |621 |


### Dataset Fields

All splits have the same fields: *premise*, *hypothesis* and *label*.
- **premise**: a string variable.
- **hypothesis**: a string variable.
- **label**: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

### Dataset Instances

An example from the "eu" split:

```
{
    "premise": "Dena idazten saiatu nintzen"
    "hypothesis": "Nire helburua gauzak idaztea zen.",
    "label": 0,
}
```



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The biases of this dataset have been studied and reported in the paper.

<!--## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section.
RELLENAR-->

**BibTeX:**
```
@inproceedings{heredia-etal-2024-xnlieu,
    title = "{XNLI}eu: a dataset for cross-lingual {NLI} in {B}asque",
    author = "Heredia, Maite  and
      Etxaniz, Julen  and
      Zulaika, Muitze  and
      Saralegi, Xabier  and
      Barnes, Jeremy  and
      Soroa, Aitor",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.234",
    pages = "4177--4188",
    abstract = "XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step. We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation. The results show that post-edition is necessary and that the translate-train cross-lingual strategy obtains better results overall, although the gain is lower when tested in a dataset that has been built natively from scratch. Our code and datasets are publicly available under open licenses.",
}
```

**APA:**

Heredia, M., Etxaniz, J., Zulaika, M., Saralegi, X., Barnes, J., & Soroa, A. (2024). XNLIeu: a dataset for cross-lingual NLI in Basque. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 4177–4188). Association for Computational Linguistics.

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