Update README.md

README.md

@@ -188,11 +188,29 @@ print("scores", model.predict(data, batch_size=8, gpus=1).scores)
 ```
 
 The training data is WMT up to 2024 (inclusive) with DA/ESA/MQM merged on a single scale.
-This model is based on the work [TODO](TODO) which can be cited as:
+This model is based on the work [COMET-poly: Machine Translation Metric Grounded in Other Candidates](https://aclanthology.org/2025.wmt-1.63/) which can be cited as:
 ```
-@misc{zuefle2025comet,
-    title={COMET-poly: Machine Translation Metric Grounded in Other Candidates},
-    author={Maike Züfle, Vilém Zouhar, Tu Anh Dinh, Felipe Polo, Jan Niehues, Mrinmaya Sachan},
-    year={2025},
+@inproceedings{zufle-etal-2025-comet,
+    title = "{COMET}-poly: Machine Translation Metric Grounded in Other Candidates",
+    author = {Z{\"u}fle, Maike  and
+      Zouhar, Vil{\'e}m  and
+      Dinh, Tu Anh  and
+      Maia Polo, Felipe  and
+      Niehues, Jan  and
+      Sachan, Mrinmaya},
+    editor = "Haddow, Barry  and
+      Kocmi, Tom  and
+      Koehn, Philipp  and
+      Monz, Christof",
+    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.wmt-1.63/",
+    doi = "10.18653/v1/2025.wmt-1.63",
+    pages = "887--904",
+    ISBN = "979-8-89176-341-8",
+    abstract = "Automated metrics for machine translation attempt to replicate human judgment. Unlike humans, who often assess a translation in the context of multiple alternatives, these metrics typically consider only the source sentence and a single translation. This discrepancy in the evaluation setup may negatively impact the performance of automated metrics. We propose two automated metrics that incorporate additional information beyond the single translation. COMET-polycand uses alternative translations of the same source sentence to compare and contrast with the translation at hand, thereby providing a more informed assessment of its quality. COMET-polyic, inspired by retrieval-based in-context learning, takes in translations of similar source texts along with their human-labeled quality scores to guide the evaluation. We find that including a single additional translation in COMET-polycand improves the segment-level metric performance (0.079 to 0.118 Kendall{'}s tau-b correlation), with further gains when more translations are added. Incorporating retrieved examples in COMET-polyic yields similar improvements (0.079 to 0.116 Kendall{'}s tau-b correlation). We release our models publicly."
 }
 ```