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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- medical |
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pretty_name: medical-bios |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Description |
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The dataset comprises English biographies labeled with occupations and binary genders. |
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This is an occupation classification task, where bias concerning gender can be studied. |
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It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician), derived from De-Arteaga et al. (2019). |
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We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive. |
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In the former, the annotators were asked to find the rationale for the question: "Why is the person in the following short bio described as a L?", where L is the gold label occupation, e.g., nurse. |
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In the latter, the question was "Why is the person in the following short bio described as an L rather than an F", where F (foil) is another medical occupation, e.g., physician. |
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You can read more details on the dataset and the annotation process in the paper [Eberle et al. (2023)](https://arxiv.org/abs/2310.11906). |
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# Dataset Structure |
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We provide the `standard` version of the dataset, where examples look as follows. |
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```json |
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{ |
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"text": "He has been a practicing Dentist for 20 years. He has done BDS. He is currently associated with Sree Sai Dental Clinic in Sowkhya Ayurveda Speciality Clinic, Chennai. ... ", |
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"label": 3, |
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} |
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``` |
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and the newly curated subset of examples including human rationales, dubbed `rationales', where examples look as follows. |
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```json |
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{ |
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"text": "'She is currently practising at Dr Ravindra Ratolikar Dental Clinic in Narayanguda, Hyderabad.", |
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"label": 3, |
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"foil": 2, |
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"words": ['She', 'is', 'currently', 'practising', 'at', 'Dr', 'Ravindra', 'Ratolikar', 'Dental', 'Clinic', 'in', 'Narayanguda', ',', 'Hyderabad', '.'] |
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"rationales": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
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"contrastive_rationales": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
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"annotations": [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...] |
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"contrastive_annotations": [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ...] |
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} |
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``` |
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# Use |
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To load the `standard` version of the dataset: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("coastalcph/medical-bios", "standard") |
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``` |
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To load the newly curated subset of examples with human rationales: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("coastalcph/medical-bios", "rationales") |
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``` |
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# Citation |
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[*Oliver Eberle\*, Ilias Chalkidis\*, Laura Cabello, Stephanie Brandl. Rather a Nurse than a Physician - Contrastive Explanations under Investigation. 2023. In the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore.*](https://aclanthology.org/2023.emnlp-main.427) |
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``` |
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@inproceedings{eberle-etal-2023-rather, |
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title = "Rather a Nurse than a Physician - Contrastive Explanations under Investigation", |
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author = "Eberle, Oliver and |
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Chalkidis, Ilias and |
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Cabello, Laura and |
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Brandl, Stephanie", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.427", |
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} |
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``` |
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