Datasets:
File size: 5,056 Bytes
93d678e b5a2ab4 2ae0cf6 efeb907 49035f3 efeb907 e668f3c efeb907 49035f3 93d678e efeb907 b5a2ab4 78d9d36 590726b 6085ab9 78d9d36 2badc00 78d9d36 b5a2ab4 590726b b5a2ab4 78d9d36 b5a2ab4 590726b 6085ab9 b5a2ab4 2badc00 c599b9f b5a2ab4 2badc00 c599b9f b5a2ab4 |
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 |
---
license: cc-by-3.0
task_categories:
- text-classification
language:
- en
pretty_name: Argument-Quality-Ranking-30k
size_categories:
- 10K<n<100K
configs:
- config_name: argument_quality_ranking
data_files:
- split: train
path: "train.csv"
- split: validation
path: "dev.csv"
- split: test
path: "test.csv"
- config_name: argument_topic
data_files:
- split: train
path: "train_topic.csv"
- split: validation
path: "dev_topic.csv"
- split: test
path: "test_topic.csv"
---
# Dataset Card for Argument-Quality-Ranking-30k Dataset
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Argument Quality Ranking](#argument-quality-ranking)
- [Argument Topic](#argument-topic)
- [Dataset Collection](#dataset-collection)
- [Argument Collection](#argument-collection)
- [Quality and Stance Labeling](#quality-and-stance-labeling)
- [Dataset Structure](#dataset-structure)
- [Quality Labels](#quality-labels)
- [Stance Labels](#stance-labels)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Summary
### Argument Quality Ranking
The dataset contains 30,497 crowd-sourced arguments for 71 debatable topics labeled for quality and stance, split into train, validation and test sets.
The dataset was originally published as part of our paper: [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://arxiv.org/abs/1911.11408).
### Argument Topic
This subset contains 9,487 of the arguments only with their topics with a different train-validation-test split. Usage of this subset TBA.
## Dataset Collection
### Argument Collection
For the purpose of collecting arguments for this dataset we conducted a crowd annotation task. We selected 71 common controversial topics for which arguments were collected (e.g., We should abolish capital punishment).
Annotators were presented with a single topic each time, and asked to contribute one supporting and one contesting argument for it, requiring arguments to be written using original language. To motivate high-quality contributions, contributors were informed they will receive extra payment for high quality arguments, as determined by the subsequent argument quality labeling task.
It was explained that an argument will be considered as a high quality one, if a person preparing a speech on the topic will be likely to use this argument as is in her speech.
We place a limit on argument length - a minimum of 35 characters and a maximum of 210 characters. In total, we collected 30,497 arguments from 280 contributors, each contributing no more than 6 arguments per topic.
### Quality and Stance Labeling
Annotators were presented with a binary question per argument, asking if they would recommend a friend to use that argument as is in a speech supporting/contesting the topic, regardless of personal opinion.
In addition, annotators were asked to mark the stance of the argument towards the topic (pro or con).
10 annotators labeled each instance.
## Dataset Structure
Each instance contains a string argument, a string topic, and quality and stance scores:
* WA - the quality label according to the weighted-average scoring function
* MACE-P - the quality label according to the MACE-P scoring function
* stance_WA - the stance label according to the weighted-average scoring function
* stance_WA_conf - the confidence in the stance label according to the weighted-average scoring function
### Quality Labels
For an explanation of the quality labels presented in columns WA and MACE-P, please see section 4 in the paper.
### Stance Labels
There were three possible annotations for the stance task: 1 (pro), -1 (con) and 0 (neutral). The stance_WA_conf column refers to the weighted-average score of the winning label. The stance_WA column refers to the winning stance label itself.
## Licensing Information
The datasets are released under the following licensing and copyright terms:
* (c) Copyright [Wikipedia](https://en.wikipedia.org/wiki/Wikipedia:Copyrights#Reusers.27_rights_and_obligations)
* (c) Copyright IBM 2014. Released under [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
## Citation Information
```
@article{DBLP:journals/corr/abs-1911-11408,
author = {Shai Gretz and
Roni Friedman and
Edo Cohen{-}Karlik and
Assaf Toledo and
Dan Lahav and
Ranit Aharonov and
Noam Slonim},
title = {A Large-scale Dataset for Argument Quality Ranking: Construction and
Analysis},
journal = {CoRR},
volume = {abs/1911.11408},
year = {2019},
url = {http://arxiv.org/abs/1911.11408},
eprinttype = {arXiv},
eprint = {1911.11408},
timestamp = {Tue, 03 Dec 2019 20:41:07 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-11408.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |