Recent machine translation algorithms mainly rely on parallel corpora. However, since the
availability of parallel corpora remains limited, only some resource-rich language pairs can …

Most current machine translation models are mainly trained with parallel corpora, and their
translation accuracy largely depends on the quality and quantity of the corpora. Although …

Machine translation has recently achieved impressive performance thanks to recent
advances in deep learning and the availability of large-scale parallel corpora. There have …

The primary objective of our work is to build a large-scale English–Thai dataset for training
neural machine translation models. We construct scb-mt-en-th-2020, an English–Thai …

Web-crawled data provides a good source of parallel corpora for training machine
translation models. It is automatically obtained, but extremely noisy, and recent work shows …

The translation quality of machine translation systems depends on the parallel corpus used
for training, particularly on the quantity and quality of the corpus. However, building a high …

Machine translation systems achieve near human-level performance on some languages,
yet their effectiveness strongly relies on the availability of large amounts of parallel …

Despite the fact that document-level machine translation has inherent advantages over
sentence-level machine translation due to additional information available to a model from …

Large-scale parallel corpora are indispensable to train highly accurate machine translators.
However, manually constructed large-scale parallel corpora are not freely available in many …

The overreliance on large parallel corpora significantly limits the applicability of machine
translation systems to the majority of language pairs. Back-translation has been dominantly …