Improve reference to mGeNTE

README.md

@@ -44,7 +44,7 @@ task_ids:
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-# 🚨 Check out [**mGeNTE**](https://huggingface.co/datasets/FBK-MT/mGeNTE), new multilingual release of the corpus.  
+# 🚨 GeNTE has been superseded by [**mGeNTE**](https://huggingface.co/datasets/FBK-MT/mGeNTE), a new multilingual release of the corpus with additional annotations.  
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 # Dataset Card for GeNTE
@@ -52,14 +52,21 @@ task_ids:
 **Homepage:** https://mt.fbk.eu/gente/
 
 
-### Dataset Summary
+## 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`).
+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`).
 
+This version of GeNTE has been used in the following papers:
+* [_Hi Guys_ or _Hi Folks_? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://aclanthology.org/2023.emnlp-main.873/) (Piergentili et al., 2023 – EMNLP)
+* [A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://aclanthology.org/2024.eacl-short.23/) (Savoldi et al., 2024 – EACL)
+* [ItaEval and TweetyIta: A New Extensive Benchmark and Efficiency-First Language Model for Italian](https://ceur-ws.org/Vol-3878/6_main_long.pdf) (Attanasio et al., 2024 – CLiC-it)
+* [ItaEval: A CALAMITA Challenge](https://ceur-ws.org/Vol-3878/117_calamita_long.pdf) (Attanasio et al., 2024 – CALAMITA)
+* [GFG - Gender-Fair Generation: A CALAMITA Challenge](https://ceur-ws.org/Vol-3878/122_calamita_long.pdf) (Frenda et al., 2024 – CALAMITA)
 
+If you plan to use GeNTE in your work, please refer to the updated corpus available at [FBK-MT/mGeNTE](https://huggingface.co/datasets/FBK-MT/mGeNTE). This version should only be used for reproducibility purposes.
 
 ### Supported Tasks and Languages
 
@@ -67,19 +74,21 @@ GeNTE comprises 1500 parallel sentences, which are enriched with manual annotati
 
 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.
+To evaluate with GeNTE you can use the gender-neutrality classifier available on Hugging Face at [FBK-MT/GeNTE-evaluator](https://huggingface.co/FBK-MT/GeNTE-evaluator).
 
-The evaluation code is available at [fbk-NEUTR-evAL](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md).
+For additional details on evaluation with GeNTE, please 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/).
+
+To reproduce our results check out the code 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: 
+The dataset consists of two configuration types (`main` and `common`) corresponding 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
+- **`GeNTE_common.tsv`:** Subset of the GeNTE corpus that comprises 3 alternative gender-neutral reference translations
 
 
 ### Data Fields