Izvorni znanstveni članak
https://doi.org/10.20532/cit.2021.1005316
Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering
Tran Hong-Viet
orcid.org/0000-0002-4675-2123
; University of Economic and Technical Industries, Hanoi, Vietnam
Nguyen Van-Vinh
; University of Engineering Technology, Vietnamese National University, Hanoi, Vietnam
Nguyen Hoang-Quan
; University of Engineering Technology, Vietnamese National University, Hanoi, Vietnam
Sažetak
The problems in machine translation are related to the characteristics of a family of languages, especially syntactic divergences between languages. In the translation task, having both source and target languages in the same language family is a luxury that cannot be relied upon. The trained models for the task must overcome such differences either through manual augmentations or automatically inferred capacity built into the model design. In this work, we investigated the impact of multiple methods of differing word orders during translation and further experimented in assimilating the source languages syntax to the target word order using pre-ordering. We focused on the field of extremely low-resource scenarios. We also conducted experiments on practical data augmentation techniques that support the reordering capacity of the models through varying the target objectives, adding the secondary goal of removing noises or reordering broken input sequences. In particular, we propose methods to improve translat on quality with the denoising autoencoder in Neural Machine Translation (NMT) and pre-ordering method in Phrase-based Statistical Machine Translation (PBSMT). The experiments with a number of English-Vietnamese pairs show the improvement in BLEU scores as compared to both the NMT and SMT systems.
Ključne riječi
Hrčak ID:
274353
URI
Datum izdavanja:
21.3.2022.
Posjeta: 491 *