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Datasets:
UKPLab
/
m2qa

Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
German
Chinese
Turkish
Size:
10K - 100K
Libraries:
Datasets
pandas
License:
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metadata
license: cc-by-nd-4.0
language:
  - de
  - zh
  - tr
size_categories:
  - 10K<n<100K
multilinguality:
  - multilingual
pretty_name: M2QA
task_categories:
  - question-answering
task_ids:
  - extractive-qa
dataset_info:
  - config_name: m2qa.german.creative_writing
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 2083548
        num_examples: 1500
    download_size: 2047695
    dataset_size: 2083548
  - config_name: m2qa.german.news
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 2192833
        num_examples: 1500
      - name: train
        num_bytes: 1527473
        num_examples: 1500
    download_size: 2438496
    dataset_size: 3720306
  - config_name: m2qa.german.product_reviews
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 1652573
        num_examples: 1500
      - name: train
        num_bytes: 1158154
        num_examples: 1500
    download_size: 1830972
    dataset_size: 2810727
  - config_name: m2qa.chinese.creative_writing
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 1600001
        num_examples: 1500
    download_size: 1559229
    dataset_size: 1600001
  - config_name: m2qa.chinese.news
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 1847465
        num_examples: 1500
      - name: train
        num_bytes: 1135914
        num_examples: 1500
    download_size: 2029530
    dataset_size: 2983379
  - config_name: m2qa.chinese.product_reviews
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 1390223
        num_examples: 1500
      - name: train
        num_bytes: 1358895
        num_examples: 1500
    download_size: 1597724
    dataset_size: 2749118
  - config_name: m2qa.turkish.creative_writing
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 1845140
        num_examples: 1500
    download_size: 1808676
    dataset_size: 1845140
  - config_name: m2qa.turkish.news
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
        num_bytes: 2071770
        num_examples: 1500
      - name: train
        num_bytes: 1362485
        num_examples: 1500
    download_size: 2287668
    dataset_size: 3434255
  - config_name: m2qa.turkish.product_reviews
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: context
        dtype: string
      - name: answers
        struct:
          - name: text
            sequence: string
          - name: answer_start
            sequence: int64
    splits:
      - name: validation
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configs:
  - config_name: m2qa.chinese.creative_writing
    data_files:
      - split: validation
        path: m2qa.chinese.creative_writing/validation-*
  - config_name: m2qa.chinese.news
    data_files:
      - split: validation
        path: m2qa.chinese.news/validation-*
      - split: train
        path: m2qa.chinese.news/train-*
  - config_name: m2qa.chinese.product_reviews
    data_files:
      - split: validation
        path: m2qa.chinese.product_reviews/validation-*
      - split: train
        path: m2qa.chinese.product_reviews/train-*
  - config_name: m2qa.german.creative_writing
    data_files:
      - split: validation
        path: m2qa.german.creative_writing/validation-*
  - config_name: m2qa.german.news
    data_files:
      - split: validation
        path: m2qa.german.news/validation-*
      - split: train
        path: m2qa.german.news/train-*
  - config_name: m2qa.german.product_reviews
    data_files:
      - split: validation
        path: m2qa.german.product_reviews/validation-*
      - split: train
        path: m2qa.german.product_reviews/train-*
  - config_name: m2qa.turkish.creative_writing
    data_files:
      - split: validation
        path: m2qa.turkish.creative_writing/validation-*
  - config_name: m2qa.turkish.news
    data_files:
      - split: validation
        path: m2qa.turkish.news/validation-*
      - split: train
        path: m2qa.turkish.news/train-*
  - config_name: m2qa.turkish.product_reviews
    data_files:
      - split: validation
        path: m2qa.turkish.product_reviews/validation-*

M2QA: Multi-domain Multilingual Question Answering

M2QA (Multi-domain Multilingual Question Answering) is an extractive question answering benchmark for evaluating joint language and domain transfer. M2QA includes 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing.

This Hugging Face datasets repo accompanies our paper "M2QA: Multi-domain Multilingual Question Answering". If you want an explanation and code to reproduce all our results or want to use our custom-built annotation platform, have a look at our GitHub repository: https://github.com/UKPLab/m2qa

Loading & Decrypting the Dataset

Following Jacovi et al. (2023), we encrypt the validation data to prevent leakage of the dataset into LLM training datasets. But loading the dataset is still easy:

To load the dataset, you can use the following code:

from datasets import load_dataset
from cryptography.fernet import Fernet

# Load the dataset
subset = "m2qa.german.news" # Change to the subset that you want to use
dataset = load_dataset("UKPLab/m2qa", subset)

# Decrypt it
fernet = Fernet(b"aRY0LZZb_rPnXWDSiSJn9krCYezQMOBbGII2eGkN5jo=")

def decrypt(example):
    example["question"] = fernet.decrypt(example["question"].encode()).decode()
    example["context"] = fernet.decrypt(example["context"].encode()).decode()
    example["answers"]["text"] = [fernet.decrypt(answer.encode()).decode() for answer in example["answers"]["text"]]
    return example

dataset["validation"] = dataset["validation"].map(decrypt)

The M2QA dataset is licensed under a "no derivative" agreement. To prevent contamination of LLM training datasets and thus preserve the dataset's usefulness to our research community, please upload the dataset only in encrypted form. Additionally, please use only APIs that do not utilize the data for training.

Overview / Data Splits

All used text passages stem from sources with open licenses. We list the licenses here: https://github.com/UKPLab/m2qa/tree/main/m2qa_dataset

We have validation data for the following domains and languages:

Subset Name Domain Language #Question-Answer instances
m2qa.german.product_reviews product_reviews German 1500
m2qa.german.creative_writing creative_writing German 1500
m2qa.german.news news German 1500
m2qa.chinese.product_reviews product_reviews Chinese 1500
m2qa.chinese.creative_writing creative_writing Chinese 1500
m2qa.chinese.news news Chinese 1500
m2qa.turkish.product_reviews product_reviews Turkish 1500
m2qa.turkish.creative_writing creative_writing Turkish 1500
m2qa.turkish.news news Turkish 1500

Additional Training Data

We also provide training data for five domain-language pairs, consisting of 1500 question-answer instances each, totalling 7500 training examples. These are the subsets that contain training data:

  • m2qa.chinese.news
  • m2qa.chinese.product_reviews
  • m2qa.german.news
  • m2qa.german.product_reviews
  • m2qa.turkish.news

The training data is not encrypted.

Citation

If you use this dataset, please cite our paper:

@inproceedings{englander-etal-2024-m2qa,
    title = "M2QA: Multi-domain Multilingual Question Answering",
    author = {Engl{\"a}nder, Leon  and
      Sterz, Hannah  and
      Poth, Clifton A  and
      Pfeiffer, Jonas  and
      Kuznetsov, Ilia  and
      Gurevych, Iryna},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.365",
    pages = "6283--6305",
}

License

This dataset is distributed under the CC-BY-ND 4.0 license.

Following Jacovi et al. (2023), we decided to publish with a "No Derivatives" license to mitigate the risk of data contamination of crawled training datasets.