metadata

license: afl-3.0
task_categories:
  - text-generation
language:
  - it
  - en
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: piqa.train.json
      - split: validation
        path: piqa.validation.json

PIQA - Italian (IT)

This dataset is an Italian translation of PIQA. PIQA stands for Physical Interaction Question Answering, a dataset of questions about common scenarios that require an understanding of the physical world.

Dataset Details

The dataset consists of questions about common scenarios that require an understanding of the physical world. Each question is associated with a correct answer and a distractor. The task is to predict the correct answer to the question.

The dataset includes the following splits:

Train: 16,044 rows
Validation: 1,830 rows

Differences with the original dataset

Did you know that not all questions in PIQA are questions? In the original dataset, some instances are not questions but text completions, statements, or even single words. In this version, we categorize all instances so as to give the possibility to filter out non-question instances or treat them differently.
The number of instances in this dataset is smaller than the original dataset due to the translation process, during which some instances were filtered out.

Languages

This dataset is fully parallel between English and Italian. This allows us to have comparable evaluation setups and results across the two languages.

Translation Process

The translation has been carried out using 🍱 OBenTO-LLM, an open-source tool for LLM-based translation. The main motivation for using an open-source LLM is to encourage free, open, reproducible, and transparent research in LLM evaluation. See 🍱 OBenTO-LLM for more details on the translation process.

Other Information

Original dataset by: Bisk et al.
Translation by: Simone Conia
Languages: Italian, English
License: AFL 3.0

Dataset Format

This is an example that shows the format of the dataset, where:

id: a unique ID for each sample;
category: type of task;
input_text: the original English sentence in the dataset;
input_text_translation: the translation of the sentence in Italian;
choices: the original English choices;
choice_translations: the translation of the choices in Italian;
gold_index: the index of the correct answer.

Example of a question in PIQA

{
  "id": "piqa_3",
  "category": "question",
  "input_text": "How do you shake something?",
  "input_text_translation": "Come si fa a scuotere qualcosa?",
  "choices": [
    "Move it up and down and side to side quickly.",
    "Stir it very quickly."
  ],
  "choice_translations": [
    "Si deve muovere rapidamente avanti e indietro e da un lato all'altro.",
    "Si mescola molto velocemente."
  ],
  "gold_index": 0
}

Example of a text completion in PIQA

{
  "id": "piqa_1",
  "category": "text_completion",
  "input_text": "To permanently attach metal legs to a chair, you can",
  "input_text_translation": "Per fissare in modo permanente le gambe di metallo a una sedia, si può",
  "choices": [
    "weld the metal together to get it to stay firmly in place.",
    "nail the metal together to get it to stay firmly in place."
  ],
  "choice_translations": [
    "saldare il metallo per farlo rimanere saldamente in posizione.",
    "incollare il metallo per farlo rimanere saldamente in posizione."
  ],
  "gold_index": 0
}

Example of a "topic" in PIQA

{
  "id": "piqa_29",
  "category": "topic",
  "input_text": "Soothe a painful sunburn.",
  "input_text_translation": "Alleviare una scottatura solare dolorosa.",
  "choices": [
    "Wait until brewed tea bag is cool, then apply on burn.",
    "Wait until brewed tea bag is hot, then apply on burn."
  ],
  "choice_translations": [
    "Attendere fino a quando il sacchetto del tè in infusione è freddo, quindi applicarlo sulla scottatura.",
    "Attendere fino a quando il sacchetto del tè in infusione è caldo, quindi applicarlo sulla scottatura."
  ],
  "gold_index": 0
}

Example of a "property" in PIQA

{
  "id": "piqa_855",
  "category": "property",
  "input_text": "Sleeves:",
  "input_text_translation": "Maniche:",
  "choices": [
    "Can be cut by sciscors with ease.",
    "Can be cut by a knife with ease."
  ],
  "choice_translations": [
    "possono essere tagliate facilmente con le forbici.",
    "possono essere tagliate con facilità con un coltello."
  ],
  "gold_index": 0
}

License

The dataset is distributed under the AFL 3.0 license.

Acknowledgements

I would like to thank the authors of the original dataset for making it available to the research community. I would also like to thank Future AI Research for supporting this work and funding my research.

Special Thanks

My special thanks go to:

Pere-Lluís Huguet Cabot and Riccardo Orlando for their help with 🍱 OBenTO-LLM.

Dataset Card Authors

Simone Conia: simone.conia@uniroma1.it