Annotator-Embeddings

Cleaned repo for our paper You Are What You Annotate: Towards Better Models through Annotator Representations at Findings of EMNLP 2023.

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Citation

@misc{deng2023annotate,
      title={You Are What You Annotate: Towards Better Models through Annotator Representations}, 
      author={Naihao Deng and Xinliang Frederick Zhang and Siyang Liu and Winston Wu and Lu Wang and Rada Mihalcea},
      year={2023},
      eprint={2305.14663},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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What our paper is about

• Rather than aggregating labels, we propose a setting of training models to directly learn from data that contains inherent disagreements.

• We propose TID-8, The Iherent Disagreement - 8 dataset, a benchmark that consists of eight existing language understanding datasets that have inherent annotator disagreements.

• We propose weighted annotator and annotation embeddings, which are model-agnostic and improve model performances on six out of the eight datasets in TID-8.

• We conduct a detailed analysis on the performance variations of our methods and how our methods can be potentially grounded to realworld demographic features.

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Structure of this repo

├── ablation_studies: scripts for ablations
│   ├── annotation_tendencies
│   ├── annotator_accs
│   ├── disagreement_examples
│   ├── heatmaps
│   ├── performance_ablation
│   ├── person_annotation_bars
│   ├── spider_plots
│   └── tsne_plots
├── experiment-results: raw experimental results and the processing script
└── src
    ├── example-data: data for each dataset
    └── src: modeling scripts
        ├── baseline_models
        ├── dataset
        ├── metrics
        ├── tokenization
        ├── training_paradigm
        ├── transformer_models
        └── utils

You may create the python environment by using the environment.yml file.

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Other links to resources for our paper

• TID-8 dataset on huggingface, here are more details of the processing scripts for each dataset.
• Project Website