BIBLIOS

Tipo
Artigos em Conferência

Tipo de Documento
Artigo Completo

Título
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

Participantes na publicação
Vânia Mendonça (Author)
Ricardo Rei (Author)
INSTITUTO SUPERIOR TÉCNICO
Luisa Coheur (Author)
INSTITUTO SUPERIOR TÉCNICO
Alberto Sardinha (Author)
INSTITUTO SUPERIOR TÉCNICO
Ana Lúcia Santos (Author)
FACULDADE DE LETRAS DA UL

Resumo
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT’19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.


Evento
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Identificadores da Publicação

Local
Online

Editora
Association for Computational Linguistics


Identificadores do Documento
DOI - https://doi.org/10.18653/v1/2021.acl-long.242
URL - http://dx.doi.org/10.18653/v1/2021.acl-long.242

Identificadores de Qualidade
CORE A* (2021) -