| Download | - View final version: Measuring sentence parallelism using Mahalanobis distances: the NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task (PDF, 252 KiB)
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| DOI | Resolve DOI: https://doi.org/10.18653/v1/W18-6481 |
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| Author | Search for: Littell, Patrick1; Search for: Larkin, Samuel1; Search for: Stewart, Darlene1; Search for: Simard, Michel1; Search for: Goutte, Cyril1; Search for: Lo, Chi-Kiu1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | The Third Conference on Machine Translation (WMT 18), Oct. 31 - Nov.1, 2018, Brussels, Belgium |
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| Abstract | 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. |
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| Publication date | 2018-11-01 |
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| Publisher | Association for Computational Linguistics (ACL) |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c7a0017b-bde5-4154-90be-93d0a454b094 |
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| Record created | 2019-04-08 |
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| Record modified | 2020-05-30 |
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