Datasets:
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K - 100K
License:
File size: 6,236 Bytes
6cf62b1 a3bd80f 6cf62b1 fa64346 6cf62b1 a3bd80f 7f20b17 a3bd80f 23ce608 a3bd80f 23ce608 33e7302 23ce608 a3bd80f 23ce608 7f20b17 a3bd80f 33e7302 a3bd80f 23ce608 a3bd80f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
---
pretty_name: NLQuAD
language:
- en
license:
- cc-by-3.0
size_categories:
- 10K<n<100K
multilinguality:
- monolingual
task_ids:
- extractive-qa
dataset_info:
features:
- name: title
dtype: string
- name: date
dtype: string
- name: paragraphs
list:
- name: context
dtype: string
- name: qas
list:
- name: answers
list:
- name: answer_end
dtype: int64
- name: answer_start
dtype: int64
- name: text
dtype: string
- name: id
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 72036724
num_examples: 10259
- name: test
num_bytes: 9045482
num_examples: 1280
- name: validation
num_bytes: 8876137
num_examples: 1280
download_size: 0
dataset_size: 89958343
---
# Dataset Card for "NLQuAD"
## 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
- **Homepage:** [https://github.com/ASoleimaniB/NLQuAD](https://github.com/ASoleimaniB/NLQuAD)
- **Paper: https://aclanthology.org/2021.eacl-main.106/**
- **Size of the generated dataset:** 89.95 MB
### 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
An example of 'train' looks as follows.
```json
{
"title": "Khashoggi murder: Body 'dissolved in acid'",
"date": "2 November 2018",
"paragraphs":[
{
"context": "A top Turkish official, presidential adviser Yasin Aktay, has said ....",
"qas":[
{
"question":"What was said in the crown prince's alleged phone call?",
"id":"0_0",
"answers":[
{
"text":"During the call with President Donald Trump\'s son-in-law Jared Kushner and national ....",
"answer_start":1352,
"answer_end": 2108,
}
]
},
{
"question":"What has the investigation found so far?",
"id":"0_1",
"answers":[
{
"text":"There is still no consensus on how Khashoggi died. He entered ....",
"answer_start":2109,
"answer_end": 3128,
}
]
},
]
}
]
}
```
### Data Fields
The data fields are the same among all splits.
- `title`: a `string` feature.
- `date`: a `string` feature.
- `paragraphs`: a list feature containing dictionaries:
- `context`: a `string` feature.
- `qas`: a list feature containing dictionaries:
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a list feature containing dictionaries:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_end`: a `int32` feature
### Data Splits
| name |train|test|validation|
|----------|----:|----:|---------:|
| |10259| 1280| 1280|
## 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.",
}
``` |