Abstract
We describe an approach to creating a small but diverse corpus in English that can be used to elicit information about any target language. The focus of the corpus is on structural information. The resulting bilingual corpus can then be used for natural language processing tasks such as inferring transfer mappings for Machine Translation. The corpus is sufficiently small that a bilingual user can translate and word-align it within a matter of hours. We describe how the corpus is created and how its structural diversity is ensured. We then argue that it is not necessary to introduce a large amount of redundancy into the corpus. This is shown by creating an increasingly redundant corpus and observing that the information gained converges as redundancy increases.
This research was funded in part by NSF grant number IIS-0121631.
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© 2004 Springer-Verlag Berlin Heidelberg
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Probst, K., Lavie, A. (2004). A Structurally Diverse Minimal Corpus for Eliciting Structural Mappings Between Languages. In: Frederking, R.E., Taylor, K.B. (eds) Machine Translation: From Real Users to Research. AMTA 2004. Lecture Notes in Computer Science(), vol 3265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30194-3_24
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DOI: https://doi.org/10.1007/978-3-540-30194-3_24
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