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 …

This paper illustrates Huawei's submission to the WMT20 low-resource parallel corpus
filtering shared task. Our approach focuses on developing a proxy task learner on top of a …

We posed the shared task of assigning sentence-level quality scores for a very noisy corpus
of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high …

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 …

Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018,
2019), we posed again the challenge of assigning sentence-level quality scores for very …

Scaling natural language processing (NLP) to low‐resourced languages to improve
machine translation (MT) performance remains enigmatic. This research contributes to the …

The paper describes parallel corpus filtering methods that allow reducing noise of noisy
“parallel” corpora from a level where the corpora are not usable for neural machine …

Abstract Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the
challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs …

Neural machine translation, which achieves near human-level performance in some
languages, strongly relies on the large amounts of parallel sentences, which hinders its …

This paper describes the submission of RWTH Aachen University for the De→ En parallel
corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT …