Update README.md

README.md

@@ -13,28 +13,7 @@ Oneirogen ([0.5B](https://huggingface.co/gustavecortal/oneirogen-0.5B), [1.5B](h
 
 Oneirogen was used to produce [The Android and The Machine](https://huggingface.co/datasets/gustavecortal/the-android-and-the-human), an English dataset composed of 10,000 real and 10,000 generated dreams.
 
-Oneirogen can be used to generate novel dream narratives. It can also be used for dream analysis. For example, one could finetuned this model on Hall and Van de Castle annotations to predict character and emotion in dream narratives. This task has been introduced by [Cortal](https://aclanthology.org/2024.lrec-main.1282/): 
-
-```
-@inproceedings{cortal-2024-sequence-sequence,
-    title = "Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives",
-    author = "Cortal, Gustave",
-    editor = "Calzolari, Nicoletta  and
-      Kan, Min-Yen  and
-      Hoste, Veronique  and
-      Lenci, Alessandro  and
-      Sakti, Sakriani  and
-      Xue, Nianwen",
-    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
-    month = may,
-    year = "2024",
-    address = "Torino, Italia",
-    publisher = "ELRA and ICCL",
-    url = "https://aclanthology.org/2024.lrec-main.1282",
-    pages = "14717--14728",
-    abstract = "The study of dreams has been central to understanding human (un)consciousness, cognition, and culture for centuries. Analyzing dreams quantitatively depends on labor-intensive, manual annotation of dream narratives. We automate this process through a natural language sequence-to-sequence generation framework. This paper presents the first study on character and emotion detection in the English portion of the open DreamBank corpus of dream narratives. Our results show that language models can effectively address this complex task. To get insight into prediction performance, we evaluate the impact of model size, prediction order of characters, and the consideration of proper names and character traits. We compare our approach with a large language model using in-context learning. Our supervised models perform better while having 28 times fewer parameters. Our model and its generated annotations are made publicly available.",
-}
-```
+Oneirogen can be used to generate novel dream narratives. It can also be used for dream analysis. For example, one could finetuned this model on [Hall and Van de Castle annotations](https://dreams.ucsc.edu/Coding/) to predict character and emotion in dream narratives. I've introduced this task in this [paper](https://aclanthology.org/2024.lrec-main.1282/): 
 
 ## Inspiration