This article presents a new semanticbased transfer approach developed and applied within the Verbmobil Machine Translation project. We give an overview of the declarative transfer formalism together with its procedural realization. Our approach is discussed and compared with several other approaches from the MT literature. The results presented in this article have been implemented and integrated into the Verbmobil system.
While automatic term extraction is a wellresearched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on predicting technicality. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general-vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.
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