Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task

By Patrick Littell, Samuel Larkin, Darlene A. Stewart, Michel Simard, Cyril Goutte, Chi-kiu (羅致翹) Lo. 2018

8 citations recorded by the source corpus.

The WMT18 shared task on parallel corpus filtering (Koehn et al., 2018b) challenged teams to score sentence pairs from a large high-recall, low-precision web-scraped parallel corpus (Koehn et al., 2018a). Participants could use existing sample corpora (e.g. past WMT data) as a supervisory signal to learn what a “clean” corpus looks like. However, in lower-resource situations it often happens that the target corpus of the language is the only sample of parallel text in that language. We therefore made several unsupervised entries, setting ourselves an additional constraint that we not utilize the additional clean parallel corpora. One such entry fairly consistently scored in the top ten systems in the 100M-word conditions, and for one task—translating the European Medicines Agency corpus (Tiedemann, 2009)—scored among the best systems even in the 10M-word conditions.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Parallel Corpus Filtering via Pre-trained Language Models (2020, background): “Other works based on sentence embeddings include Hangya and Fraser (2018) and Littell et al. (2018), as well as Schwenk et al. (2019), which mines millions of parallel sentences in 1620 language pairs from Wikipedia.”
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions (2019, methodology): “Littell et al. (2018) used monolingual sentence embedding spaces to discount outliers.”