apiergentili
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README.md update
Browse files# Why is the change needed?
This changes introduces a revised and updated README file.
# What changes does the patch introduce?
The new version of the `README.md` includes tags and metadata required by HuggingFace to properly visualize the corpus, as well as corrections and improvements.
# How was this patch tested?
No tests are required.
README.md
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---
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license: cc-by-4.0
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task_categories:
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- translation
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Neo-GATE is a bilingual corpus designed to benchmark the ability of machine translation (MT) systems to translate from English into Italian using gender-inclusive neomorphemes.
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It is built upon GATE [(Rarrick et al., 2023)](https://dl.acm.org/doi/10.1145/3600211.3604675), a benchmark for the evaluation of gender rewriters and gender bias in MT.
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Neo-GATE includes 841 `test` entries and 100 `dev` entries.
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The source sentences are gender-ambiguous, i.e. they provide no information about the gender of human referents.
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In
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Neo-GATE allows for the evaluation of any neomorpheme paradigm in Italian.
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For more details see the [Adaptation](#adaptation) section below.
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`Neo-GATE.tsv` includes the following columns:
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- **#:**
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- **GATE-ID:**
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- **SPLIT:** Either `dev` or `test`, indicating whether the entry belongs to the dev set or the test set.
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- **SOURCE:** The English source sentence.
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- **REF-M:** The Italian reference where all gender-marked terms are masculine.
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- **REF-F:** The Italian reference where all gender-marked terms are feminine.
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- **REF-TAGGED:** The Italian reference where all gender-marked terms are tagged with Neo-GATE's annotation.
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- **ANNOTATION:** The
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## Dataset creation
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Please refer to [the original paper](https://arxiv.org/
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## Adaptation
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To adapt Neo-GATE to the desired neomorpheme paradigm, a `.json` file mapping Neo-GATE's tagset to the desired forms is required.
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See `schwa.json` for an example.
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For more information on the tagset, see Table 8 in [the original paper](https://arxiv.org/
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To create the adapted references and annotations, use the `neo-gate_format.py` script with the following syntax:
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This command will create two files: `OUTPUT_FILE_NAME.ref`, containing the adapted references, and `OUTPUT_FILE_NAME.ann`, containing the adapted annotations.
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For instance, to generate the references and the annotations adapted to the
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python neo-gate_adapt.py --tagset schwa.json --out neogate_schwa
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This will create the two files `neogate_schwa.ref` and `neogate_schwa.ann`.
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## Evaluation
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The evaluation code
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## Licensing Information
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The Neo-GATE corpus is released under a Creative Commons Attribution 4.0 International license (CC BY 4.0).
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## Citation
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If you use Neo-GATE in your work, please
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---
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configs:
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- config_name: main
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data_files:
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- split: test
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path: Neo-GATE.tsv
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- split: dev
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path: Neo-GATE-dev.tsv
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default: true
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license: cc-by-4.0
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task_categories:
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- translation
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Neo-GATE is a bilingual corpus designed to benchmark the ability of machine translation (MT) systems to translate from English into Italian using gender-inclusive neomorphemes.
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It is built upon GATE [(Rarrick et al., 2023)](https://dl.acm.org/doi/10.1145/3600211.3604675), a benchmark for the evaluation of gender rewriters and gender bias in MT.
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Neo-GATE includes 841 `test` entries (`Neo-GATE.tsv`) and 100 `dev` entries (`Neo-GATE-dev.tsv`).
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Each entry is composed of an English source sentence, three Italian references which only differ for the presence of either masculine/feminine/nonbinary words, and the annotation of the target words that are relevant for the evaluation of gender-inclusive MT.
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The source sentences are gender-ambiguous, i.e. they provide no information about the gender of human referents.
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In this setting, words referring to human entities in the target language should be rendered with neomorphemes, special characters or symbols that replace masculine and feminine inflectional morphemes.
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Neo-GATE allows for the evaluation of any neomorpheme paradigm in Italian.
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For more details see the [Adaptation](#adaptation) section below.
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`Neo-GATE.tsv` includes the following columns:
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- **#:** Neo-GATE unique identifier.
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- **GATE-ID:** A unique identifier of the entry in GATE, composed of a prefix indicating the subset of origin within GATE (e.g., `IT_2_variants`) followed by a serial number indicating the position of the entry within that subset (i.e., `001`, `002`, etc.).
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- **SOURCE:** The English source sentence.
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- **REF-M:** The Italian reference where all gender-marked terms are masculine.
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- **REF-F:** The Italian reference where all gender-marked terms are feminine.
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- **REF-TAGGED:** The Italian reference where all gender-marked terms are tagged with Neo-GATE's annotation.
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- **ANNOTATION:** The word level annotation.
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## Dataset creation
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Please refer to [the original paper](https://arxiv.org/abs/2405.08477) for full details on dataset creation.
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## Curation rationale
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Neo-GATE was designed to allow for the evalution of gender-inclusive MT and to be adaptable to any neomorpheme paradigm in Italian.
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To this aim, the original Italian references found in GATE were edited so as to have placeholder tags in place of gendered morphemes and function words (articles, possessive adjectives, etc.) referred to human entities.
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The tags were designed to cover all parts of the grammar which express grammatical gender, and to be replaced with corresponding forms in the desired neomorpheme paradigm.
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## Adaptation
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To adapt Neo-GATE to the desired neomorpheme paradigm, a `.json` file mapping Neo-GATE's tagset to the desired forms is required.
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See `schwa.json` or `asterisk.json` for an example.
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For more information on the tagset, see Table 8 in [the original paper](https://arxiv.org/abs/2405.08477).
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To create the adapted references and annotations, use the `neo-gate_format.py` script with the following syntax:
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This command will create two files: `OUTPUT_FILE_NAME.ref`, containing the adapted references, and `OUTPUT_FILE_NAME.ann`, containing the adapted annotations.
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For instance, to generate the references and the annotations adapted to the Schwa paradigm provided in the example file `schwa.json`, the following command can be used:
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python neo-gate_adapt.py --tagset schwa.json --out neogate_schwa
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This will create the two files `neogate_schwa.ref` and `neogate_schwa.ann`.
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By default, the script will adapt references and annotations found in `Neo-GATE.tsv`.
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If the `Neo-GATE.tsv` file is located in a different directory, or if you wish to use a different file (e.g., the dev set split file `Neo-GATE-dev.tsv`), you can specify the path to the file with the optional argument `--neogate`.
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## Evaluation
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The evaluation code will soon be available at [fbk-NEUTR-evAL](https://github.com/hlt-mt/fbk-NEUTR-evAL/tree/main).
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## Dataset Curators
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- Andrea Piergentili (FBK): apiergentili@fbk.eu
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- Beatrice Savoldi (FBK): bsavoldi@fbk.eu
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- Luisa Bentivogli (FBK): bentivo@fbk.eu
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## Licensing Information
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The Neo-GATE corpus is released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). See the [LICENSE](LICENSE) file for details.
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## Citation
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If you use Neo-GATE in your work, please cite the following paper:
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@misc{piergentili2024enhancing,
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title={Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models},
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author={Andrea Piergentili and Beatrice Savoldi and Matteo Negri and Luisa Bentivogli},
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year={2024},
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eprint={2405.08477},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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## Contributions
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Thanks to @apiergentili for adding this dataset.
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