Update README.md

README.md

@@ -36,45 +36,7 @@ ds = load_dataset("jhacken/GAND")
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 ## Cite this dataset
-@inproceedings{hackenbuchner-etal-2026-explaining,
-    title = "Explaining {GAND}: A Resource on Gender-Ambiguous Natural Data {\&} Contrastive Attribution",
-    author = "Hackenbuchner, Jani{\c{c}}a  and
-      Degraeuwe, Jasper  and
-      Tezcan, Arda  and
-      Daems, Joke",
-    editor = "Shterionov, Dimitar  and
-      Vanmassenhove, Eva  and
-      De Sisto, Mirella  and
-      Blain, Fred  and
-      Pourmostafa Roshan Sharami, Javad  and
-      Lepp, Lisa  and
-      Manna, Chiara  and
-      Rescigno, Argentina Anna  and
-      Karakanta, Alina  and
-      Rigouts Terryn, Ayla  and
-      Lardelli, Manuel  and
-      Resende, Natalia  and
-      Murgolo, Elena  and
-      Hackenbuchner, Jani{\c{c}}a  and
-      Zaretskaya, Anna  and
-      Espl{\`a}-Gomis, Miquel  and
-      Etchegoyhen, Thierry  and
-      Gromann, Dagmar  and
-      Bawden, Rachel  and
-      Haddow, Barry  and
-      Szoc, Sara  and
-      Forcada, Mikel  and
-      Moniz, Helena",
-    booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
-    month = jun,
-    year = "2026",
-    address = "Tilburg, The Netherlands",
-    publisher = "European Association for Machine Translation",
-    url = "https://aclanthology.org/2026.eamt-1.18/",
-    pages = "245--267",
-    ISBN = "9789403901411",
-    abstract = "Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender ambiguous scenarios in a natural way. To this end, we present GAND, a gender ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifically designed to analyse the influence of contextual cues on gender in translation. We leverage GAND to conduct an interpretability analysis: we translate a subset of GAND into two grammatical gender languages and extend these with manually crafted contrastive translations. A following feature attribution analysis reveals source words in context that inform the gender translation of an ambiguous referent entity in the target translation. As a newly introduced resource, GAND is designed to be benchmarked across diverse target languages and evaluated with a wide range of MT systems."
-}
+Janiça Hackenbuchner, Jasper Degraeuwe, Arda Tezcan, and Joke Daems. 2026. Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 245–267, Tilburg, The Netherlands. European Association for Machine Translation.
 
 ## Acknowledgements
 GAND was developed as part of a strategic basic PhD research (1SH5V24N) fully funded by The Research Foundation – Flanders (FWO) for the time span of four years,