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CoMeDiNLP: Context and Meaning--Navigating Disagreements in NLP Annotations https://unimplicit.github.io/
Workshop held in conjunction with COLING 2025 January 19/20, 2025
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Disagreements among annotators pose a significant challenge in Natural Language Processing, impacting the quality and reliability of datasets and consequently the performance of NLP models. This workshop aims to explore the complexities of annotation disagreements, their causes, and strategies towards their effective resolution, with a focus on meaning in context.
The quality and reliability of annotated data is crucial for the development of robust NLP models. However, managing disagreements among annotators poses significant challenges to researchers and practitioners. Such disagreements can stem from various factors, including subjective interpretations, cultural biases and ambiguous guidelines. Early research has highlighted the impact of annotator disagreements on data quality and model performance (e.g. Artstein and Poesio, 2008; Pustejovsky and Stubbs, 2012; Plank et al., 2014).
More recent work on perspectivism in NLP, such as that by Basile et al. (2021), highlights the importance of embracing multiple perspectives in annotation tasks to better capture the diversity of human language. This approach argues for the inclusion of various viewpoints to improve the robustness and fairness of NLP models. On the modeling side, various methods for dealing with annotation disagreements have been proposed. For example, Hovy et al. (2013) and Passonneau and Carpenter (2014) identify and weigh annotator reliability to better aggregate contributions, whereas recent approaches following the perspectivism approach leverage inherent disagreements in subjective tasks to train models handling diverse opinions (Davani et al., 2022; Deng et al., 2023).
== Call for Submissions ==
We invite both long (8 pages) and short (4 page) papers. The limits refer to the content and any number of additional pages for references are allowed. The papers should follow the COLING 2025 formatting instructions.
Each submission must be anonymized, written in English, and contain a title and abstract. We especially welcome papers that address the following themes, for a single type of disagreement or annotation disagreements in general:
- New benchmarks for detecting or categorizing disagreements - Models and modeling strategies for variations in annotation - Evaluation schemes and metrics for phenomena without a single ground truth - Phenomena that are not yet within reach with current NLP technology.
To encourage discussion and community building and to bootstrap potential collaborations, we elicit, in addition to shared task papers and regular "archival" track papers, also non-archival submissions. These can take 2 forms:
- Works in progress, that are not yet mature enough for a full submission, can be submitted in the form of a title and abstract. Abstracts may be up to two pages in length. - Already published work, or work currently under submission elsewhere, can be submitted in the form of an abstract and a copy of the submission/publication.
These works will be reviewed for topical fit and accepted submissions will be presented as posters. Depending on the final workshop program, selected works may be presented in panels. We plan for these to be an opportunity for researchers to present and discuss their work with the relevant community.
Please submit your papers here: https://softconf.com/coling2025/CM-ND-NLP25/
== Important Dates ==
November 18, 2024: Due date for workshop and shared task papers [1] December 1-3, 2024: Author response period December 5, 2024: Notification of acceptance December 13, 2024: Camera-ready submission deadline January 19/20, 2025: Workshop date
All deadlines are 11:59pm UTC-12 ("anywhere on Earth").
[1] If you plan to submit a paper but require a deadline extension, please send us an email to michael.roth@utn.de and dominik.schlechtweg@ims.uni-stuttgart.de
== Organizers ==
Michael Roth, University of Technology Nuremberg Dominik Schlechtweg, University of Stuttgart
== Program Committee ==
David Alfter, University of Gothenburg Valerio Basile, University of Turin Felipe Bravo, University of Chile Jing Chen, Hong Kong Polytechnic University Naihao Deng, University of Michigan Aida Mostafazadeh Davani, Google Research Diego Frassinelli, University of Konstanz / LMU Munich Haim Dubossarsky, Queen Mary University Simon Hengchen, iguanodon.ai & Université de Genève Sandra Kübler, Indiana University Andrei Kutuzov, University of Oslo Elisa Leonardelli, Fondazione Bruno Kessler Marie-Catherine de Marneffe, UCLouvain Maja Pavlovic, Queen Mary University Siyao Peng, LMU Munich Pauline Sander, University of Stuttgart Pia Sommerauer, Vrije Universiteit Amsterdam Nina Tahmasebi, University of Gothenburg Alexandra Uma Frank D. Zamora-Reina, University of Chile Wei Zhao, University of Aberdeen
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Prof. Michael Roth [he/him]
Natural Language Understanding Lab
University of Technology Nuremberg Technische Universität Nürnberg
We moved the deadline for regular and non-archival papers to *Friday*, November 22 (11:59pm UTC-12).
********************************************************************************
CoMeDiNLP: Context and Meaning--Navigating Disagreements in NLP Annotations https://comedinlp.github.io/
Workshop held in conjunction with COLING 2025 January 19/20, 2025
********************************************************************************
Disagreements among annotators pose a significant challenge in Natural Language Processing, impacting the quality and reliability of datasets and consequently the performance of NLP models. This workshop aims to explore the complexities of annotation disagreements, their causes, and strategies towards their effective resolution, with a focus on meaning in context.
The quality and reliability of annotated data is crucial for the development of robust NLP models. However, managing disagreements among annotators poses significant challenges to researchers and practitioners. Such disagreements can stem from various factors, including subjective interpretations, cultural biases and ambiguous guidelines. Early research has highlighted the impact of annotator disagreements on data quality and model performance (e.g. Artstein and Poesio, 2008; Pustejovsky and Stubbs, 2012; Plank et al., 2014).
More recent work on perspectivism in NLP, such as that by Basile et al. (2021), highlights the importance of embracing multiple perspectives in annotation tasks to better capture the diversity of human language. This approach argues for the inclusion of various viewpoints to improve the robustness and fairness of NLP models. On the modeling side, various methods for dealing with annotation disagreements have been proposed. For example, Hovy et al. (2013) and Passonneau and Carpenter (2014) identify and weigh annotator reliability to better aggregate contributions, whereas recent approaches following the perspectivism approach leverage inherent disagreements in subjective tasks to train models handling diverse opinions (Davani et al., 2022; Deng et al., 2023).
== Call for Submissions ==
We invite both long (8 pages) and short (4 page) papers. The limits refer to the content and any number of additional pages for references are allowed. The papers should follow the COLING 2025 formatting instructions.
Each submission must be anonymized, written in English, and contain a title and abstract. We especially welcome papers that address the following themes, for a single type of disagreement or annotation disagreements in general:
- New benchmarks for detecting or categorizing disagreements - Models and modeling strategies for variations in annotation - Evaluation schemes and metrics for phenomena without a single ground truth - Phenomena that are not yet within reach with current NLP technology.
To encourage discussion and community building and to bootstrap potential collaborations, we elicit, in addition to shared task papers and regular "archival" track papers, also non-archival submissions. These can take 2 forms:
- Works in progress, that are not yet mature enough for a full submission, can be submitted in the form of a title and abstract. Abstracts may be up to two pages in length. - Already published work, or work currently under submission elsewhere, can be submitted in the form of an abstract and a copy of the submission/publication.
These works will be reviewed for topical fit and accepted submissions will be presented as posters. Depending on the final workshop program, selected works may be presented in panels. We plan for these to be an opportunity for researchers to present and discuss their work with the relevant community.
Please submit your papers here: https://softconf.com/coling2025/CM-ND-NLP25/
== Important Dates ==
November 18, 2024: Due date for system description papers December 1-3, 2024: Author response period (for system description papers)
*NEW* November 22, 2024: Due date for regular and non-archival workshop papers
December 6, 2024: Notification of acceptance for all papers December 13, 2024: Camera-ready submission deadline for all papers January 19/20, 2025: Workshop date
== Organizers ==
Michael Roth, University of Technology Nuremberg Dominik Schlechtweg, University of Stuttgart
== Program Committee ==
David Alfter, University of Gothenburg Valerio Basile, University of Turin Felipe Bravo, University of Chile Jing Chen, Hong Kong Polytechnic University Naihao Deng, University of Michigan Aida Mostafazadeh Davani, Google Research Diego Frassinelli, University of Konstanz / LMU Munich Haim Dubossarsky, Queen Mary University Simon Hengchen, iguanodon.ai & Université de Genève Sandra Kübler, Indiana University Andrei Kutuzov, University of Oslo Elisa Leonardelli, Fondazione Bruno Kessler Marie-Catherine de Marneffe, UCLouvain Maja Pavlovic, Queen Mary University Siyao Peng, LMU Munich Pauline Sander, University of Stuttgart Pia Sommerauer, Vrije Universiteit Amsterdam Nina Tahmasebi, University of Gothenburg Alexandra Uma Frank D. Zamora-Reina, University of Chile Wei Zhao, University of Aberdeen
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Prof. Michael Roth [he/him]
Natural Language Understanding Lab
University of Technology Nuremberg Technische Universität Nürnberg
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Michael Roth