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12

ACL
2010

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Tackling Sparse Data Issue in Machine Translation Evaluation

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Tackling Sparse Data Issue in Machine Translation Evaluation

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We illustrate and explain problems of n-grams-based machine translation (MT) metrics (e.g. BLEU) when applied to morphologically rich languages such as Czech. A novel metric SemPOS based on the deep-syntactic representation of the sentence tackles the issue and retains the performance for translation to English as well.

Ondrej Bojar, Kamil Kos, David Marecek

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ACL 2010 | Computational Linguistics | Deep-syntactic Representation | N-grams-based Machine Translation | Rich Languages |

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Added	10 Feb 2011
Updated	10 Feb 2011
Type	Journal
Year	2010
Where	ACL
Authors	Ondrej Bojar, Kamil Kos, David Marecek

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