Machine translation is highly sensitive to the size and quality of the training data, which has
led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we …

We learn a joint multilingual sentence embedding and use the distance between sentences
in different languages to filter noisy parallel data and to mine for parallel data in large news …

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 …

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 …

This paper presents an effective approach for parallel corpus mining using bilingual
sentence embeddings. Our embedding models are trained to produce similar …

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 …

We report on methods to create the largest publicly available parallel corpora by crawling
the web, using open source software. We empirically compare alternative methods and …

We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type
of bag-of-words translation feature, and train logistic regression models to classify good data …

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 …

In this work we introduce dual conditional cross-entropy filtering for noisy parallel data. For
each sentence pair of the noisy parallel corpus we compute cross-entropy scores according …