Update README.md

README.md

@@ -28,18 +28,18 @@ widget:
 
 ### Datasets
 
-We used the [Propicto-orféo dataset](https://www.ortolang.fr/market/corpora/propicto), which was created from the CEFC-Orféo corpus. 
+The [Propicto-orféo dataset](https://www.ortolang.fr/market/corpora/propicto) is used, which was created from the CEFC-Orféo corpus. 
 This dataset was presented in the research paper titled ["A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation](https://aclanthology.org/2024.lrec-main.76/)" at LREC-Coling 2024. The dataset was split into training, validation, and test sets.
 
 | **Split** | **Number of utterances** |
-|-----------|:-----------------------:|
+|:-----------:|:-----------------------:|
 | train | 231,374 |
 | valid | 28,796 |
 | test | 29,009 |
 
 ### Parameters
 
-A full list of the parameters is available in the config.json file. We specified the following arguments in the training pipeline :
+A full list of the parameters is available in the config.json file. This is the arguments in the training pipeline :
 
 ```python
 training_args = Seq2SeqTrainingArguments(
@@ -66,7 +66,7 @@ The model was evaluated with [sacreBLEU](https://huggingface.co/spaces/evaluate-
 
 Comparison to other translation models :
 | **Model** | **validation** | **test** |
-|-----------|:-----------------------:|:-----------------------:|
+|:-----------:|:-----------------------:|:-----------------------:|
 | **t2p-t5-large-orféo** | 85.2 | 85.8 |
 | t2p-nmt-orféo | **87.2** | **87.4** | 
 | t2p-mbart-large-cc25-orfeo | 75.2 | 75.6 |
@@ -74,7 +74,7 @@ Comparison to other translation models :
 
 ### Environmental Impact
 
-
+Fine-tuning was performed using a single Nvidia V100 GPU with 32 GB of memory which took 16 hours in total.
 
 ## Using t2p-t5-large-orféo model with HuggingFace transformers
 
@@ -94,6 +94,8 @@ outputs = model.generate(inputs.to("cuda:0"), max_new_tokens=40, do_sample=True,
 pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
 ```
 
+## Linking and viewing the predicted sequence of tokens to the corresponding ARASAAC pictograms
+
 ## Information
 
 - **Language(s):** French
@@ -115,5 +117,15 @@ pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
 If you use this model for your own research work, please cite as follows:
 
 ```bibtex
-
+@inproceedings{macaire_jeptaln2024,
+  title = {{Approches cascade et de bout-en-bout pour la traduction automatique de la parole en pictogrammes}},
+  author = {Macaire, C{\'e}cile and Dion, Chlo{\'e} and Schwab, Didier and Lecouteux, Benjamin and Esperan{\c c}a-Rodier, Emmanuelle},
+  url = {https://inria.hal.science/hal-04623007},
+  booktitle = {{35{\`e}mes Journ{\'e}es d'{\'E}tudes sur la Parole (JEP 2024) 31{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN 2024) 26{\`e}me Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL 2024)}},
+  address = {Toulouse, France},
+  publisher = {{ATALA \& AFPC}},
+  volume = {1 : articles longs et prises de position},
+  pages = {22-35},
+  year = {2024}
+}
 ```