Create README.md

README.md

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+---
+language:
+- nl
+base_model:
+- FacebookAI/xlm-roberta-base
+pipeline_tag: token-classification
+tags:
+- coreference
+- resolution
+- gender
+- neutral
+- pronouns
+- debiading
+- dutch
+- CDA
+---
+This Dutch coreference resolution model is based on the [wl-coref](https://github.com/vdobrovolskii/wl-coref) model. The model was debiased through applying CDA, in order to improve its performance on gender-neutral pronouns and neopronouns. We used [XLM-RoBERTa-base](https://huggingface.co/FacebookAI/xlm-roberta-base) as our base model, fine-tuned the model on the [SoNaR-1 corpus](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/), and then further fine-tuned the model on a gender-neutral version of the this corpus for debiasing purposes. We used five different seeds during our experiment, and upload the five versions of the model.
+
+For more information, see our [Github repository](https://github.com/gvanboven/Transforming_Dutch). 
+
+This model was created as part of our FAccT 2024 paper. Find our published paper at https://dl.acm.org/doi/10.1145/3630106.3659049 and arxiv paper at https://arxiv.org/abs/2405.00134. 
+
+## Usage
+For usage instructions for the wl-coref model, see [their repo](https://github.com/vdobrovolskii/wl-coref). 
+
+## Citation
+G. van Boven, Y. Du, D. Nguyen, _Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns_. FAccT 2024.
+
+```
+@inproceedings{boven-2024-transforming,
+title = "Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns",
+author = "van Boven, Goya and Du, Yupei and Nguyen, Dong",
+booktitle = "Proceedings of the 2024 Conference on Fairness, Accountability, and Transparency",
+month = jun,
+year = "2024",
+address = "Online and Rio de Janeiro, Brazil",
+publisher = "Association for Computing Machinery"
+}
+```