Large parallel corpora that are automatically obtained from the web, documents or elsewhere often exhibit many corrupted parts that are bound to negatively affect the quality of the systems and models that learn from these corpora. This paper describes frequent problems found in data and such data affects neural machine translation systems, as well as how to identify and deal with them. The solutions are summarised in a set of scripts that remove problematic sentences from input corpora.
Impact of Corpora Quality on Neural Machine Translation
Data corruption in large parallel corpora can degrade the quality of neural machine translation systems; the paper outlines methods for identifying and removing problematic sentences.
- Year
- 2018
- Venue
- arXiv 2018
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- 1
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/1810.08392ARXIV-DEFAULT
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