README.md

---
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
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    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
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    num_examples: 1500
  - name: train
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    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
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    num_examples: 1500
  download_size: 1808676
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- 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
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    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
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    struct:
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    - name: answer_start
      sequence: int64
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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](https://aclanthology.org/2024.findings-emnlp.365/)". 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](https://github.com/UKPLab/m2qa)


Loading & Decrypting the Dataset
-----------------

Following [Jacovi et al. (2023)](https://aclanthology.org/2023.emnlp-main.308/), 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:
```python
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](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](https://creativecommons.org/licenses/by-nd/4.0/legalcode).

Following [Jacovi et al. (2023)](https://aclanthology.org/2023.emnlp-main.308/), we decided to publish with a "No Derivatives" license to mitigate the risk of data contamination of crawled training datasets.