Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling

Raheel Qader (1) , François Portet (2, 1) , Cyril Labbé (3)

1 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
2 LIG - Laboratoire d'Informatique de Grenoble
3 SIGMA - Systèmes d’Information - inGénierie et Modélisation Adaptables

We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known models such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit.

Mots clés: sequence-to-sequence modeling; deep learning; toolkit

Domaines: Intelligence artificielle [cs.AI]

Raheel Qader, François Portet, Cyril Labbé. Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling. LREC 2020, May 2020, Marseille, France. pp.7140-7144. ⟨hal-02895652⟩