matteogabburo/mTRECQA

Dataset Description

mTRECQA originates from TREC-QA, which is created from the TREC 8 to TREC 13 QA tracks. TREC 8-12 constitutes the training set, while TREC 13 questions are set aside for development and testing.

The dataset has been translated into five European languages: French, German, Italian, Portuguese, and Spanish, as described in this paper: Datasets for Multilingual Answer Sentence Selection.

Splits:

For each language (English, French, German, Italian, Portuguese, and Spanish), we provide:

• train split
• validation split
• test split

How to load them:

To use these splits, you can use the following snippet of code replacing [LANG] with a language identifier (en, fr, de, it, po, sp)

from datasets import load_dataset

# if you want the whole corpora
corpora = load_dataset("matteogabburo/mTRECQA")

"""
if you want the default splits of a specific language, replace [LANG] with an identifier in: en, fr, de, it, po, sp
dataset = load_dataset("matteogabburo/mTRECQA", "[LANG]")
"""
# example:
italian_dataset = load_dataset("matteogabburo/mTRECQA", "it")

Format:

Each example has the following format:

{
  'eid': 42588,
  'qid': 1003,
  'cid': 4,
  'label': 1,
  'question': 'In welchem Land liegt die heilige Stadt Mekka?',
  'candidate': 'Der französische Präsident Jacques Chirac hat heute sein Beileid ausgedrückt, wegen des Todes von 250 Pilgern bei einem Brand, der am Dienstag in einem Lager in der Nähe der heiligen Stadt Mekka in Saudi-Arabien ausbrach.'
}

Where:

• eid: is the unique id of the example (question, candidate)
• qid: is the unique id of the question
• cid: is the unique id of the answer candidate
• label: identifies whether the answer candidate candidate is correct for the question (1 if correct, 0 otherwise)
• question: the question
• candidate: the answer candidate

Citation

If you find this dataset useful, please cite the following paper:

BibTeX:

@misc{gabburo2024datasetsmultilingualanswersentence,
      title={Datasets for Multilingual Answer Sentence Selection}, 
      author={Matteo Gabburo and Stefano Campese and Federico Agostini and Alessandro Moschitti},
      year={2024},
      eprint={2406.10172},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.10172}, 
}