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Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation

Labaka, Gorka and Stroppa, Nicolas and Way, Andy and Sarasola, Kepa (2007) Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation. In: Machine Translation Summit XI, 10-14 September, 2007, Copenhagen, Denmark.

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In this paper, we compare the rule-based and data-driven approaches in the context of Spanish-to-Basque Machine Translation. The rule-based system we consider has been developed specifically for Spanish-to-Basque machine translation, and is tuned to this language pair. On the contrary, the data-driven system we use is generic, and has not been specifically designed to deal with Basque. Spanish-to-Basque Machine Translation is a challenge for data-driven approaches for at least two reasons. First, there is lack of bilingual data on which a data-driven MT system can be trained. Second, Basque is a morphologically-rich agglutinative language and translating to Basque requires a huge generation of morphological information, a difficult task for a generic system not specifically tuned to Basque. We present the results of a series of experiments, obtained on two different corpora, one being “in-domain” and the other one “out-of-domain” with respect to the data-driven system. We show that n-gram based automatic evaluation and edit-distance-based human evaluation yield two different sets of results. According to BLEU, the data-driven system outperforms the rule-based system on the in-domain data, while according to the human evaluation, the rule-based approach achieves higher scores for both corpora.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Spanish language; Basque language;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
Publisher:European Association for Machine Translation
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI OS/IN/1732
ID Code:15228
Deposited On:18 Feb 2010 14:00 by DORAS Administrator. Last Modified 27 Apr 2010 14:44

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