t2p-nmt-orfeo

t2p-nmt-orfeo

t2p-nmt-orfeo is a text-to-pictograms translation model built by training from scratch the NMT model on a dataset of pairs of transcriptions / pictogram token sequence (each token is linked to a pictogram image from ARASAAC). The model is used only for inference.

Training details

The model was trained with Fairseq.

Datasets

The Propicto-orféo dataset 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" at LREC-Coling 2024. The dataset was split into training, validation, and test sets.

Parameters

This is the arguments in the training pipeline :

fairseq-train \
    data-bin/orfeo.tokenized.fr-frp \
    --arch transformer_iwslt_de_en --share-decoder-input-output-embed \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
    --dropout 0.3 --weight-decay 0.0001 \
    --save-dir exp_orfeo/checkpoints/nmt_fr_frp_orfeo \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 4096 \
    --eval-bleu \
    --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
    --eval-bleu-detok moses \
    --eval-bleu-remove-bpe \
    --eval-bleu-print-samples \
    --best-checkpoint-metric bleu --maximize-best-checkpoint-metric \
    --max-epoch 40 \
    --keep-best-checkpoints 5 \
    --keep-last-epochs 5

Evaluation

The model was evaluated with sacreBLEU, where we compared the reference pictogram translation with the model hypothesis.

fairseq-generate exp_orfeo/data-bin/orfeo.tokenized.fr-frp \
    --path exp_orfeo/checkpoints/nmt_fr_frp_orfeo/checkpoint.best_bleu_87.2803.pt \
    --batch-size 128 --beam 5 --remove-bpe > gen_orfeo.out

The output file prints the following information :

S-16709	peut-être vous pouvez vous exprimer
T-16709	vous pouvoir exprimer
H-16709	-0.0769597738981247	vous pouvoir exprimer
D-16709	-0.0769597738981247	vous pouvoir exprimer
P-16709	-0.0936 -0.0924 -0.0065 -0.1154
Generate test with beam=5: BLEU4 = 87.43, 95.2/89.8/85.0/80.4 (BP=1.000, ratio=1.006, syslen=250949, reflen=249520)

Results

Comparison to other translation models :

Environmental Impact

Training was performed using a single Nvidia V100 GPU with 32 GB of memory which took around 2 hours in total.

Using t2p-nmt-orfeo model

The scripts to use the t2p-nmt-orfeo model are located in the speech-to-pictograms GitHub repository.

Information

• Language(s): French
• License: Apache-2.0
• Developed by: Cécile Macaire
• Funded by
  • GENCI-IDRIS (Grant 2023-AD011013625R1)
  • PROPICTO ANR-20-CE93-0005
• Authors
  • Cécile Macaire
  • Chloé Dion
  • Emmanuelle Esperança-Rodier
  • Benjamin Lecouteux
  • Didier Schwab

Citation

If you use this model for your own research work, please cite as follows:

@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}
}