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README (1).md

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+---
+configs:
+  - config_name: main
+    data_files:
+      - split: test
+        path: "GeNTE.tsv"
+    default: true
+  - config_name: common
+    data_files:
+      - split: test
+        path: "GeNTE_common.tsv"
+
+
+
+annotations_creators:
+- expert-generated
+language:
+- en
+- it
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+- translation
+paperswithcode_id: null
+pretty_name: 'GeNTE: Gender-Neutral Translation Evaluation'
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags:
+- gender
+- bias
+- inclusivity
+- rewriting
+- translation
+- mt
+task_categories:
+- translation
+- text-generation
+task_ids:
+- language-modeling
+---
+
+# Dataset Card for GeNTE
+
+**Homepage:** https://mt.fbk.eu/gente/
+
+
+### Dataset Summary
+
+GeNTE (**Ge**nder-**N**eutral **T**ranslation **E**valuation) is a natural, bilingual corpus designed to benchmark the ability of machine translation systems to generate gender-neutral translations.
+
+Built from European Parliament speeches, GeNTE comprises a subset of the English-Italian portion of the [Europarl corpus](https://www.statmt.org/europarl/archives.html). 
+GeNTE comprises 1500 parallel sentences, which are enriched with manual annotations and feature a balanced distribution of translation phenomena that either entail i) a gender-neutral translation (`set-N`), or ii) a gendered translation in the target language (`set-G`).
+
+
+
+### Supported Tasks and Languages
+
+**Machine Translation**
+
+GeNTE supports cross-lingual (en-it) and intra-lingual (it-it) gender inclusive translation tasks.
+
+Refer to the paper [*Hi Guys* or *Hi Folks?* Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://aclanthology.org/2023.emnlp-main.873/) for additional details on evaluation with GeNTE.
+
+The evaluation code is available at [fbk-NEUTR-evAL](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md).
+
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset consists of two configuration types (`main` and `common`) corrisponding to the files: 
+
+- **`GeNTE.tsv`:** The complete GeNTE corpus and its set annotations
+-  **`GeNTE_common.tsv`:** Subset of the GeNTE corpus that comprises 3 alternative gender-neutral reference translations
+
+
+### Data Fields
+
+**`GeNTE.tsv`** is organized into 8 tab-separated columns as follows: 
+
+	 - ID: The unique GeNTE ID.
+	 - Europarl_ID: The original sentence ID from Europarl's common-test-set 2.
+	 - SET: Indicates whether the entry belongs to the Set-G or the Set-N subportion of the corpus.
+	 - SRC: The English source sentence.
+	 - REF-G: The gendered Italian reference translation.
+	 - REF-N: The gender-neutral Italian reference, produced by a professional translator. 
+	 - COMMON: Indicates whether the entry is part of GeNTE common-set (yes/no).
+	 - GENDER: For entries belonging to the Set-G, indicates if the the entry is Feminine or Masculine (F/M).
+
+For entries of the common set, REF-N provides the gender-neutral Italian reference translation n. 2. 
+
+**`GeNTE-common.tsv`** comprises 200 entries organized into 9 tab-separated columns as follows:
+
+	 - ID: The unique GeNTE ID.
+	 - Europarl_ID: The original sentence ID from Europarl's common-test-set 2.
+	 - SET: Indicates whether the entry belongs to the Set-G or the Set-N subportion of the corpus.
+	 - SRC: The English source sentence.
+	 - REF-G: The gendered Italian reference translation.
+	 - REF-N1: The gender-neutral Italian reference produced by Translator 1.
+	 - REF-N2: The gender-neutral Italian reference produced by Translator 2.
+	 - REF-N3: The gender-neutral Italian reference produced by Translator 3.
+	 - GENDER: For entries belonging to the Set-G, indicates if the the entry is Feminine or Masculine (F/M).
+
+
+## Dataset Creation
+
+Refer to the original [paper](https://aclanthology.org/2023.emnlp-main.873/) for full details on dataset creation. 
+
+### Curation Rationale
+
+GeNTE is designed to evaluate models’ ability to perform gender-neutral translations under desirable circumstances. In
+fact, when referents’ gender is unknown or irrelevant, undue gender inferences should not be made
+and translation should be neutral. Instead, when a referent’s gender is relevant and
+known, MT should not over-generalize to neutral translations. The corpus hence consists parallel sentences with mentions to human referents that equally represent two
+translation scenarios: 
+
+- `Set-N`: featuring gender-ambiguous source sentences that require to be neutrally rendered in translation;
+- `Set-G`: featuring gender-unambiguous source sentences, which shall
+be properly rendered with gendered (masculine or feminine) forms in translation.
+
+### Source Data
+
+The dataset contains text data extracted and edited from the Europarl Corpus ([common test set 2](https://www.statmt.org/europarl/archives.html)), and all rights of the data belong to the European Union and/or respective copyright holders. 
+Please refer to Europarl “[Terms of Use](https://www.statmt.org/europarl/archives.html)” for details.
+
+### Annotations
+For each sentence pair extracted from Europarl (src, it-ref),GeNTE includes an additional Italian reference, which differs from the original one only in that it refers to
+the human entities with neutral expressions. 
+
+The neutral reference translation were created by professionals based on the following [guidelines](https://drive.google.com/file/d/1TvV6NQoXiPHNSUHYlf4NFhef1_PKncF6/view?usp=sharing).
+
+### Dataset Curators
+
+The authors of GeNTE are the dataset curators.
+
+- Beatrice Savoldi (FBK): [email protected]
+- Luisa Bentivogli (FBK): [email protected]
+- Andrea Piergentili (FBK): [email protected]
+
+
+### Licensing Information
+
+The GeNTE corpus is released under a Creative Commons Attribution 4.0 International license (CC BY 4.0).
+
+
+
+## Citation
+```bibtex
+@inproceedings{piergentili-etal-2023-hi,
+    title = "Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the {G}e{NTE} Corpus",
+    author = "Piergentili, Andrea and 
+      Savoldi, Beatrice  and
+      Fucci, Dennis  and
+      Negri, Matteo  and
+      Bentivogli, Luisa",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.873",
+    doi = "10.18653/v1/2023.emnlp-main.873",
+    pages = "14124--14140"
+}
+```