diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
index b2ba35b558ba..726f45d7a0b9 100644
--- a/_bibliography/papers.bib
+++ b/_bibliography/papers.bib
@@ -24,7 +24,7 @@ @inproceedings{LMCor
       abbr={EACL},
       title={Small Language Models Improve Giants by Rewriting Their Outputs}, 
       author={G. Vernikos and A. Bražinskas and J. Adamek and J. Mallinson and A. Severyn and E. Malmi},
-      booktitle = "18th Conference of the European Chapter of the Association for Computational Linguistics",
+      booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics",
       year={2024},
       abstract = {Large language models (LLMs) have demonstrated impressive few-shot learning capabilities, but they often underperform compared to fine-tuned models on challenging tasks. 
         Furthermore, their large size and restricted access only through APIs make task-specific fine-tuning impractical. 
@@ -65,7 +65,7 @@ @inproceedings{gpoet
     title = {GPoeT: a Language Model Trained for Rhyme Generation on Synthetic Data},
     author = {A. Popescu-Belis and A.R. Atrio and B. Bernath and E. Boisson and T. Ferrari and X. Theimer-lienhard and G. Vernikos},
     year = {2023},
-    booktitle = "7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
+    booktitle = "Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
     abstract = {Poem generation with language models requires the modeling of rhyming patterns. We propose a novel solution for learning to rhyme, based on synthetic data generated with a rule-based rhyming algorithm. 
     The algorithm and an evaluation metric use a phonetic dictionary and the definitions of perfect and assonant rhymes. 
     We fine-tune a GPT-2 English model with 124M parameters on 142 MB of natural poems and find that this model generates consecutive rhymes infrequently (11\%). 
@@ -81,7 +81,7 @@ @inproceedings{document_metric
     title = {Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric},
     author = {G. Vernikos and B. Thompson and P. Mathur and M. Federico},
     year = {2022},
-    booktitle = "Seventh Conference on Machine Translation (WMT)",
+    booktitle = "Proceedings of the Seventh Conference on Machine Translation",
     abstract = {We present a very simple method for extending pretrained machine translation metrics to incorporate document-level context. We apply our method to four popular metrics: BERTScore, Prism, COMET, and the reference-free metric COMET-QE. 
     We evaluate our document-level metrics on the MQM annotations from the WMT 2021 metrics shared task and find that the document-level metrics outperform their sentence-level counterparts in about 85\% of the tested conditions, when excluding results on low-quality human references. 
     Additionally, we show that our document-level extension of COMET-QE dramatically improves accuracy on discourse phenomena tasks, supporting our hypothesis that our document-level metrics are resolving ambiguities in the reference sentence by using additional context.},
@@ -97,7 +97,7 @@ @inproceedings{smala
       title={Subword Mapping and Anchoring across Languages},
       author={G. Vernikos and A. Popescu-Belis},
       year={2021},
-      booktitle = "EMNLP (Findings)",
+      booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
       selected={true},
       pdf= {https://aclanthology.org/2021.findings-emnlp.224/},
       tldr = {https://twitter.com/gvernikos/status/1438135806577758212},
@@ -119,7 +119,7 @@ @inproceedings{cal
       author={K. Margatina and G. Vernikos
        and L. Barrault and N. Aletras},
       year={2021},
-      booktitle = "EMNLP",
+      booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
       selected= {true},
       abstract = {Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. 
       In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. 
@@ -140,7 +140,7 @@ @inproceedings{atrio-etal-2021-iict
       G. Vernikos  and
       A. Popescu-Belis  and
       L. Dolamic},
-    booktitle = "WMT",
+    booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     year = {2021},
     abstract = {In this paper, we present the systems submitted by our team from the Institute of ICT (HEIG-VD / HES-SO) to the Unsupervised MT and Very Low Resource Supervised MT task. 
     We first study the improvements brought to a baseline system by techniques such as back-translation and initialization from a parent model. 
@@ -159,7 +159,6 @@ @inproceedings{vernikos-etal-2020-domain
               I. Androutsopoulos},
     booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
     year = {2020},
-    booktitle = "EMNLP (Findings)",
     pages = "3103--3112",
     selected = {false},
     abstract = {In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results.