change reference paper

README.md

@@ -8,7 +8,7 @@ language:
 The **Gender-Neutral Translation (GeNTE) Evaluator** is a sequence classification model used for evaluating inclusive rewriting and translations into Italian with the [GeNTE corpus](https://huggingface.co/datasets/FBK-MT/GeNTE). 
 It is built by fine-tuning the RoBERTa-based [UmBERTo model](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1). 
 
-More details on the training process and the reproducibility can be found in the [official repository](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md) and the [paper](https://aclanthology.org/2023.emnlp-main.873/).
+More details on the training process and the reproducibility can be found in the [official repository](https://github.com/hlt-mt/fbk-NEUTR-evAL/blob/main/solutions/GeNTE.md) and the [paper](https://aclanthology.org/2024.eacl-short.23/).
 
 ## Usage
 
@@ -39,25 +39,24 @@ print(predicted_label)  # 0 is neutral, 1 is gendered
 ## Citation
 
 ```
-@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
+@inproceedings{savoldi-etal-2024-prompt,
+    title = "A Prompt Response to the Demand for Automatic Gender-Neutral Translation",
+    author = "Savoldi, Beatrice  and
+      Piergentili, Andrea  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",
+    editor = "Graham, Yvette  and
+      Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
     publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2023.emnlp-main.873",
-    doi = "10.18653/v1/2023.emnlp-main.873",
-    pages = "14124--14140",
-  }
+    url = "https://aclanthology.org/2024.eacl-short.23",
+    pages = "256--267",
+    abstract = "Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.",
+}
 ```
 
 ## Contributions