commit message

.gitignore

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+# VariErr
+*.DS_Store
+wandb/
+.idea/
+
+# Byte-compiled / optimized / DLL files
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+#.idea/

README.md

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+# VariErr NLI: Separating Annotation Error from Human Label Variation #
+
+This repo contains the data and code introduced in the following ACL 2024 paper: 
+
+[VariErr NLI: Separating Annotation Error from Human Label Variation
+](https://arxiv.org/abs/2403.01931) 
+
+## Contributions ##
+
+- This repo introduces a systematic methodology and a new dataset, VariErr (variation versus error), focusing on the NLI task in English. 
+- VariErr includes a 2-round annotation procedure with annotators explaining each label and subsequently judging the validity of label-explanation pairs. 
+- VariErr contains 7,732 validity judgments on 1,933 explanations for 500 re-annotated MNLI items from ChaosNLI. 
+- We include in this repo codes to assess the effectiveness of various automatic error detection (AED) methods and GPTs in uncovering errors versus human label variation. 
+
+## VariErr Procedure ##
+<p align="center">
+<img src="figs/varierr.png" width="500" />
+</p>
+
+## VariErr Examples ##
+<p align="center">
+<img src="figs/table1.png" width="800" />
+</p>
+
+
+## Data ##
+
+- We release our 2-round NLI annotations in [*varierr.json*](varierr.json).
+- Dataset is also available at *https://huggingface.co/datasets/mainlp/varierr*.
+
+## Code ##
+### Please follow these steps to reproduce results in the paper:
+- Pip install required packages.
+``` 
+pip install -r requirements.txt
+```
+- Run the [*run_experiments.sh*](run_experiments.sh) bash script to train sequence classification and predict supervised/scorers/llms/baselines results. 
+``` 
+bash run_experiments.sh
+```
+- Main result [*results/results.tsv*](results/results.tsv) and additional ones are in [*results/*](results/).
+- Predictions are saved in [*predictions/*](predictions/).
+
+
+## Reference ##
+### Please cite the following paper: 
+
+```
+@inproceedings{weber-2024-varierr,
+      title={{VariErr NLI: Separating Annotation Error from Human Label Variation}}, 
+      author={Leon Weber-Genzel and Siyao Peng and Marie-Catherine de Marneffe and Barbara Plank},
+      year={2024},
+      booktitle={ACL 2024},
+}
+```
+## Acknowledgement
+- This work is funded by ERC Consolidator Grant DIALECT 101043235 and supported by project KLIMA-MEMES funded by the Bavarian Research Institute for Digital Transformation (bidt), an institute of the Bavarian Academy of Sciences and Humanities. 
+- The authors are responsible for the content of this publication.