Update README.md

README.md

@@ -1,4 +1,16 @@
 ---
+pretty_name: NLQuAD
+language:
+- en
+license:
+- cc-by-3.0
+size_categories:
+- 10K<n<100K
+multilinguality:
+- monolingual
+task_ids:
+- extractive-qa
+paperswithcode_id: nlquad-a-non-factoid-long-question-answering
 dataset_info:
   features:
   - name: title
@@ -38,4 +50,98 @@ dataset_info:
 ---
 # Dataset Card for "NLQuAD"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Repository: [Github](https://github.com/ASoleimaniB/NLQuAD)**
+- **Paper:[NLQuAD: A Non-Factoid Long Question Answering Data Set](https://aclanthology.org/2021.eacl-main.106/)**
+
+### Dataset Summary
+
+This is a copy of the original NLQuAD dataset distributed via [Github](https://github.com/ASoleimaniB/NLQuAD).
+
+NLQuAD is a non-factoid long question answering dataset from BBC news articles.
+NLQuAD’s question types and the long length of its context documents as well as answers, make it a challenging real-world task.
+NLQuAD consists of news articles as context documents, interrogative sub-headings in the articles as questions, and body paragraphs corresponding to the sub-headings as contiguous answers to the questions.
+NLQuAD contains 31k non-factoid questions and long answers collected from 13k BBC news articles.
+See example articles in BBC [1](https://www.bbc.com/news/world-asia-china-51230011), [2](https://www.bbc.com/news/world-55709428).
+We automatically extract target answers because annotating for non-factoid long QA is extremely challenging and costly.
+
+## Dataset Structure
+
+### Data Instances
+
+#### plain_text
+An example of 'train' looks as follows.
+
+```
+{
+    "answers": {
+        "answer_start": [1],
+        "text": ["This is a test text"]
+    },
+    "context": "This is a test context.",
+    "id": "1",
+    "question": "Is this a test?",
+    "title": "train test"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### plain_text
+- `id`: a `string` feature.
+- `title`: a `string` feature.
+- `context`: a `string` feature.
+- `question`: a `string` feature.
+- `answers`: a dictionary feature containing:
+  - `text`: a `string` feature.
+  - `answer_start`: a `int32` feature.
+
+
+### Data Splits
+
+|   name   |train|test|validation|
+|----------|----:|----:|---------:|
+|plain_text|87599|10570|     10570|
+
+
+## Additional Information
+
+### Licensing Information
+
+This dataset is distributed under the [CC BY-NC](https://creativecommons.org/licenses/by-nc/3.0/) licence providing free access for non-commercial and academic usage.
+
+### Citation Information
+
+BibTeX:```json
+@inproceedings{soleimani-etal-2021-nlquad,
+    title = "{NLQ}u{AD}: A Non-Factoid Long Question Answering Data Set",
+    author = "Soleimani, Amir  and
+      Monz, Christof  and
+      Worring, Marcel",
+    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
+    month = apr,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.eacl-main.106",
+    doi = "10.18653/v1/2021.eacl-main.106",
+    pages = "1245--1255",
+    abstract = "We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions. We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans. To establish baseline performances, we compare BERT, RoBERTa, and Longformer models. Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements. NLQuAD{'}s samples exceed the input limitation of most pre-trained Transformer-based models, encouraging future research on long sequence language models.",
+}
+
+```