README.md

---
annotations_creators:
- machine-generated
language:
- ar
- es
- fr
- de
- hi
- it
- ja
- nl
- pt
language_creators:
- found
license_details: https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/LICENSE.md
multilinguality:
- multilingual
- translation
pretty_name: xtr-wiki_qa
size_categories:
- 100K<n<1M
source_datasets:
- extended|wiki_qa
tags:
- as2
- answer sentence selection
- text retrieval
- question answering
task_categories:
- question-answering
- text-retrieval
task_ids:
- open-domain-qa
license: cdla-permissive-2.0
---


# Xtr-WikiQA

## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Source Data](#source-data)
  - [Additional Information](#additional-information)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Amazon Science](https://www.amazon.science/publications/cross-lingual-knowledge-distillation-for-answer-sentence-selection-in-low-resource-languages)
- **Paper:** [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://aclanthology.org/2023.findings-acl.885/)
- **Point of Contact:** [Yoshitomo Matsubara](yomtsub@amazon.com)

### Dataset Summary

***Xtr-WikiQA*** is an Answer Sentence Selection (AS2) dataset in 9 non-English languages, proposed in our paper accepted at ACL 2023 (Findings): [**Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages**](https://aclanthology.org/2023.findings-acl.885/).
This dataset is based on an English AS2 dataset, WikiQA ([Original](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0), [Hugging Face](https://huggingface.co/datasets/wiki_qa)).
For translations, we used [Amazon Translate](https://aws.amazon.com/translate/).

### Languages

- Arabic (ar)
- Spanish (es)
- French (fr)
- German (de)
- Hindi (hi)
- Italian (it)
- Japanese (ja)
- Dutch (nl)
- Portuguese (pt)

File location: [`tsv/`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/tree/main/tsv)

## Dataset Structure

### Data Instances

This is an example instance from the Arabic training split of Xtr-WikiQA dataset. 

```
{
  "QuestionID": "Q1",
  "Question": "كيف تتشكل الكهوف الجليدية؟",
  "DocumentID": "D1",
  "DocumentTitle": "كهف جليدي",
  "SentenceID": "D1-0",
  "Sentence": "كهف جليدي مغمور جزئيًا على نهر بيريتو مورينو الجليدي.",
  "Label": 0
}
```

All the translated instances in tsv files are listed in the same order of the original (native) instances in the WikiQA dataset.

For example, the 2nd instance in [`tsv/ar-train.tsv`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/tsv/ar-train.tsv) (Arabic-translated from English)
corresponds to the 2nd instance in [`WikiQA-train.tsv`](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0) (English).

### Data Fields

Each instance (a QA pair) consists of the following fields:

- `QuestionID`: Question ID (str)
- `Question`: Question to be answered (str)
- `DocumentID`: Document ID (str)
- `DocumentTitle`: Document title (str)
- `SentenceID`: Answer sentence in the document (str)
- `Sentence`: Answer sentence in the document (str)
- `Label`: Label that indicates the answer sentence correctly answers the question (int, 1: correct, 0: incorrect)


### Data Splits

|                   |             | **#Questions** |          |   |           | **#Sentences** |          |
|-------------------|------------:|---------------:|---------:|---|----------:|---------------:|---------:|
|                   |   **train** |        **dev** | **test** |   | **train** |        **dev** | **test** |
| **Each language** |         873 |            126 |      243 |   |     8,671 |          1,130 |    2,351 |

See [our paper](#citation-information) for more details about the statistics of the datasets.


## Dataset Creation

### Source Data

The source of Xtr-WikiQA dataset is [WikiQA](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0).


## Additional Information

### Licensing Information

[CDLA-Permissive-2.0](LICENSE.md)

### Citation Information

```bibtex
@inproceedings{gupta2023cross-lingual,
  title={{Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages}},
  author={Gupta, Shivanshu and Matsubara, Yoshitomo and Chadha, Ankit and Moschitti, Alessandro},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  pages={14078--14092},
  year={2023}
}
```


### Contributions

- [Shivanshu Gupta](https://huggingface.co/shivanshu)
- [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara)
- Ankit Chadha
- Alessandro Moschitti