metadata

license: cc-by-3.0
task_categories:
  - text-classification
language:
  - en
pretty_name: Argument-Quality-Ranking-30k
size_categories:
  - 10K<n<100K
dataset_info:
  splits:
    - name: train
      num_examples: 20974
    - name: dev
      num_examples: 3208
    - name: test
      num_examples: 6315

Dataset Card for Argument-Quality-Ranking-30k Dataset

Dataset Summary

The dataset contains 30,497 crowd-sourced arguments for 71 debatable topics labeled for quality and stance, split into train, dev and test sets.

The dataset was originally published as part of our paper A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis.

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:

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}
}