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LICENSE.md

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+# Community Data License Agreement - Permissive - Version 2.0
+
+This is the Community Data License Agreement - Permissive, Version 2.0 (the "agreement"). Data Provider(s) and Data Recipient(s) agree as follows:
+
+## 1. Provision of the Data
+
+1.1. A Data Recipient may use, modify, and share the Data made available by Data Provider(s) under this agreement if that Data Recipient follows the terms of this agreement.
+
+1.2. This agreement does not impose any restriction on a Data Recipient's use, modification, or sharing of any portions of the Data that are in the public domain or that may be used, modified, or shared under any other legal exception or limitation.
+
+## 2. Conditions for Sharing Data
+
+2.1. A Data Recipient may share Data, with or without modifications, so long as the Data Recipient makes available the text of this agreement with the shared Data.
+
+## 3. No Restrictions on Results
+
+3.1. This agreement does not impose any restriction or obligations with respect to the use, modification, or sharing of Results.
+
+## 4. No Warranty; Limitation of Liability
+
+4.1. All Data Recipients receive the Data subject to the following terms:
+
+THE DATA IS PROVIDED ON AN "AS IS" BASIS, WITHOUT REPRESENTATIONS, WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
+
+NO DATA PROVIDER SHALL HAVE ANY LIABILITY FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING WITHOUT LIMITATION LOST PROFITS), HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE DATA OR RESULTS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+## 5. Definitions
+
+5.1. "Data" means the material received by a Data Recipient under this agreement.
+
+5.2. "Data Provider" means any person who is the source of Data provided under this agreement and in reliance on a Data Recipient's agreement to its terms.
+
+5.3. "Data Recipient" means any person who receives Data directly or indirectly from a Data Provider and agrees to the terms of this agreement.
+
+5.4. "Results" means any outcome obtained by computational analysis of Data, including for example machine learning models and models' insights.

README.md

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+---
+annotations_creators:
+- machine-generated
+language:
+- bn
+- en
+- fi
+- id
+- ja
+- ko
+- ru
+- sw
+language_creators:
+- found
+#license:
+license_details: https://huggingface.co/datasets/AmazonScience/tydi-as2/blob/main/LICENSE.md
+multilinguality:
+- multilingual
+- translation
+pretty_name: tydi-as2
+size_categories:
+- 10M<n<100M
+source_datasets:
+- extended|tydiqa
+tags:
+- as2
+- answer sentence selection
+- text retrieval
+- question answering
+task_categories:
+- question-answering
+- text-retrieval
+task_ids:
+- open-domain-qa
+---
+
+
+# TyDi-AS2
+
+## 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)
+      - [TyDi-AS2](#tydi-as2)
+      - [Xtr-TyDi-AS2](#xtr-tydi-as2)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Source Data](#source-data)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Amazon Science]()
+- **Paper:** [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://arxiv.org/abs/2305.16302)
+- **Point of Contact:** [Yoshitomo Matsubara](yomtsub@amazon.com)
+
+### Dataset Summary
+
+***TyDi-AS2*** and ***Xtr-TyDi-AS2*** are multilingual Answer Sentence Selection (AS2) datasets comprising 8 diverse languages, proposed in our paper accepted at ACL 2023 (Findings): **Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages**.
+Both the datasets were created from [TyDi-QA](https://ai.google.com/research/tydiqa), a multilingual question-answering dataset. TyDi-AS2 was created by converting the QA instances in TyDi-QA to AS2 instances (see [Dataset Creation](#dataset-creation) for details). Xtr-TyDi-AS2 was created by translating the non-English TyDi-AS2 instances to English and vise versa.
+For translations, we used [Amazon Translate](https://aws.amazon.com/translate/).
+
+### Languages
+
+#### TyDi-AS2 (original)
+
+- `bn`: Bengali
+- `en`: English
+- `fi`: Finnish
+- `id`: Indonesian
+- `ja`: Japanese
+- `ko`: Korean
+- `ru`: Russian
+- `sw`: Swahili
+
+File location: [`jsonl/original/`](jsonl/original/)
+
+For non-English sets, we also have English-translated samples used for the cross-lingual knowledge distillation (CLKD) experiments in our paper.
+
+File location: [`jsonl/x-to-en/`](jsonl/x-to-en/)
+
+#### Xtr-TyDi-AS2 (translationese)
+
+Xtr-TyDi-AS2 (X-translated TyDi-AS2) dataset consists of non-English AS2 instances translated from the English set of TyDi-AS2.
+
+- `bn`: Bengali
+- `fi`: Finnish
+- `id`: Indonesian
+- `ja`: Japanese
+- `ko`: Korean
+- `ru`: Russian
+- `sw`: Swahili
+
+File location: [`jsonl/en-to-x/`](jsonl/en-to-x/)
+
+## Dataset Structure
+
+### Data Instances
+
+This is an example instance from the English training split of TyDi-AS2 dataset. 
+
+```
+{
+  "Question": "When was the Argentine Basketball Federation formed?",
+  "Title": "History of the Argentina national basketball team",
+  "Sentence": "The Argentina national basketball team represents Argentina in basketball international competitions, and is controlled by the Argentine Basketball Federation.",
+  "Label": 0
+}
+```
+
+For English-translated TyDi-AS2 dataset and Xtr-TyDi-AS2 dataset, the translated instances in JSONL files are listed in the same order of the original (native) instances in the original TyDi-AS2 dataset.
+
+For example, the 2nd instance in [`jsonl/x-to-en/en_from_bn-train.jsonl`](jsonl/x-to-en/en_from_bn-train.jsonl) (English-translated from Bengali) corresponds to the 2nd instance in [`jsonl/original/bn-train.jsonl`](jsonl/x-to-en/bn-train.jsonl) (Bengali).
+
+Similarly, the 2nd instance in [`jsonl/en-to-x/bn_from_en-train.jsonl`](jsonl/en-to-x/bn_from_en-train.jsonl) (Bengali-translated from English) corresponds to the 2nd instance in [`jsonl/original/en-train.jsonl`](jsonl/en-to-x/en-train.jsonl) (English).
+
+### Data Fields
+
+Each instance (a QA pair) consists of the following fields:
+
+- `Question`: Question to be answered (str)
+- `Title`: Document title (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** |
+| **Bengali (bn)**    |     7,978 |          2,056 |      316 |   | 1,376,432 |        351,186 |   37,465 |
+| **English (en)**    |     6,730 |          1,686 |      918 |   | 1,643,702 |        420,899 |  249,513 |
+| **Finnish (fi)**    |    10,859 |          2,731 |    1,870 |   | 1,567,695 |        408,205 |  298,093 |
+| **Indonesian (id)** |     9,310 |          2,339 |    1,355 |   |   960,270 |        236,076 |   97,057 |
+| **Japanese (ja)**   |    11,848 |          2,981 |    1,504 |   | 3,183,037 |        822,654 |  444,106 |
+| **Korean (ko)**     |     7,354 |          1,943 |    1,389 |   | 1,558,191 |        392,361 |  199,043 |
+| **Russian (ru)**    |     9,187 |          2,294 |    1,395 |   | 3,190,650 |        820,668 |  367,595 |
+| **Swahili (sw)**    |     8,350 |          2,850 |    1,896 |   | 1,048,303 |        269,894 |   74,775 |
+
+See [our paper](#citation-information) for more details about the statistics of the datasets.
+
+
+## Dataset Creation
+
+### Source Data
+
+The source of TyDi-AS2 dataset is [TyDi QA](https://ai.google.com/research/tydiqa), which is a question answering dataset.
+
+### Annotations
+
+#### Annotation process
+
+TyDi QA is a QA dataset spanning questions from 11 typologically diverse languages.
+Each instance comprises a human-generated question, a single Wikipedia document as context, and one or more spans from the document containing the answer.
+To convert each instance into AS2 instances, we split the context document into sentences and heuristically identify the correct asnwer sentences using the annotated answer spans.
+To split documents, we use multiple different sentence tokenizers for the diverse languages and omit languages for which we could not find a suitable sentence tokenizer:
+1. [bltk](https://github.com/saimoncse19/bltk) for Bengali
+2. [blingfire](https://github.com/microsoft/BlingFire) for Swahili, Indonesian, and Korean
+3. [pysdb](https://github.com/nipunsadvilkar/pySBD) for English and Russian
+4. [nltk](https://www.nltk.org/) for Finnish
+5. [Konoha](https://github.com/himkt/konoha) for Japanese
+
+#### Who are the annotators?
+
+[Shivanshu Gupta](https://huggingface.co/shivanshu) converted TyDi QA to TyDi-AS2.
+[Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara) translated non-English samples to English and vice versa for Xtr-TyDi-AS2 dataset
+Since sentence tokenization and identifying answer sentences can introduce errors, we conducted a manual validation of the AS2 datasets. For each language, we randomly selected 50 instances and verified the accuracy of the answer sentences through manual inspection. Our findings revealed that the answer sentences were accurate in 98% of the cases.
+
+## Additional Information
+
+### Dataset Curators
+
+Shivanshu Gupta (@shivanshu)
+
+
+### Licensing Information
+
+[CDLA-Permissive-2.0](LICENSE.md)
+
+### Citation Information
+
+```
+@article{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},
+  journal={arXiv preprint arXiv:2305.16302},
+  year={2023}
+}
+```
+
+
+### Contributions
+
+- [Shivanshu Gupta](https://huggingface.co/shivanshu)
+- [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara)
+- Ankit Chadha
+- Alessandro Moschitti

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