About
This directory contains the code used for the publication "Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity", published in the Proceedings of the 9th Workshop on Argument Mining @ Coling 2022.
This work is based on research in the project CIMT/Partizipationsnutzen, which is funded by the Federal Ministry of Education and Research as part of its Social-Ecological Research funding priority, funding no. 01UU1904. (for more information, visit https://www.cimt-hhu.de/en/)
Content
"code/data_preprocessing/" contains the code used to prepare the data basis by transforming the single concreteness ratings into a unified concreteness score and a subjectivity score (2 class schema and 4 class schema). Additionaly, the splits for the repeated 5-fold cross validation are prepared.
"code/data_analysis/" contains a script to get some statistics and correlation effects.
"code/models/" contains the code used for the experiments. "run_experiments" starts the experiments (baseline, logistic regression, random forest, SVM). "BERT.ipynb" is a Google Drive based Notebook to execute the BERT experiments.
"data/" contains the preprocessed dataset and splits.
Citation
@inproceedings{romberg-2022-perspective, title = "Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity", author = "Romberg, Julia", booktitle = "Proceedings of the 9th Workshop on Argument Mining", month = oct, year = "2022", address = "Online and in Gyeongju, Republic of Korea", publisher = "International Conference on Computational Linguistics", url = "https://aclanthology.org/2022.argmining-1.11", pages = "115--125", abstract = "Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important perspectives and in many cases cannot do justice to the subjective character of the tasks. One solution to this shortcoming are multi-perspective approaches, which have received very little attention in the field of argument mining so far. In this work we present PerspectifyMe, a method to incorporate perspectivism by enriching a task with subjectivity information from the data annotation process. We exemplify our approach with the use case of classifying argument concreteness, and provide first promising results for the recently published CIMT PartEval Argument Concreteness Corpus.", }
If you use the dataset, please cite the following paper:
@inproceedings{romberg-etal-2022-corpus, title = "A Corpus of {G}erman Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification", author = "Romberg, Julia and Mark, Laura and Escher, Tobias", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.308", pages = "2874--2883", }
License
The annotated data corpus is available under the Creative Commons CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0/).
Contact Person
Julia Romberg, [email protected], https://www.cimt-hhu.de/en/team/romberg/