| Autor: |
Escolano Peinado, Carlos, Ruiz Costa-Jussà, Marta, Rodríguez Fonollosa, José Adrián, Artetxe Zurutuza, Mikel |
| Přispěvatelé: |
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Popis: |
State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. In this paper, we propose an alternative approach that is based on language-specific encoder-decoders, and can thus be more easily extended to new languages by learning their corresponding modules. So as to encourage a common interlingua representation, we simultaneously train the N initial languages. Our experiments show that the proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average, while allowing to add new languages without the need to retrain the rest of the modules. All in all, our work closes the gap between shared and language-specific encoderdecoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings. This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947657). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
