Cite the IBERAMIA 2026 paper

README.md

@@ -26,8 +26,9 @@ Bertin-RoBERTa fine-tuned on ESNLIR through **pool-based active learning** with
 acquisition strategy. This is the best-performing round: **710,000 labels (71% of the 1M
 pool)**, selected over 7 of 10 acquisition rounds.
 
-> **Active Learning for Spanish Natural Language Inference**
+> **Active Learning for Spanish Natural Language Inference on a Heterogeneous Multi-Domain Corpus**
 > Diego Ortiz, Johan R. Portela, Ruben Manrique — Universidad de los Andes, Bogotá
+> *Advances in Artificial Intelligence — IBERAMIA 2026* (to appear)
 
 Code: [`jd-rodriguezp1234/esnlir-active-learning`](https://github.com/jd-rodriguezp1234/esnlir-active-learning)
 
@@ -111,11 +112,15 @@ Full round-by-round metrics and the exact pool indices acquired at each step are
 ## Citation
 
 ```bibtex
-@misc{ortiz_esnlir_active_learning,
-  author = {Ortiz, Diego and Portela, Johan R. and Manrique, Ruben},
-  title  = {Active Learning for Spanish Natural Language Inference},
-  school = {Universidad de los Andes, Bogot{\'a}, Colombia},
-  year   = {2026},
+@InProceedings{ortiz2026activelearningspanishnli,
+  author    = {Ortiz, Diego and Portela, Johan R. and Manrique, Ruben},
+  title     = {Active Learning for Spanish Natural Language Inference
+               on a Heterogeneous Multi-Domain Corpus},
+  booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2026},
+  year      = {2026},
+  publisher = {Springer Nature Switzerland},
+  address   = {Cham},
+  note      = {To appear},
 }
 ```