tõlkeandmed

Tõlkeandmed refers to the data used in the field of machine translation, particularly in the context of neural machine translation (NMT). It encompasses various forms of linguistic information that train and evaluate translation models. The most common type of tõlkeandmed is parallel corpora, which consist of pairs of sentences or documents that are translations of each other. These parallel texts are essential for supervised NMT models, as they provide the input-output examples the model learns from.

Beyond parallel corpora, tõlkeandmed can also include monolingual data, which is text in a single language. Monolingual data is valuable for improving the fluency and naturalness of translated output, as it can be used in techniques like language modeling. Furthermore, tõlkeandmed can incorporate linguistic annotations such as part-of-speech tags, dependency parses, or named entity recognition labels. These annotated datasets can aid in developing more sophisticated NMT models that leverage deeper linguistic understanding.

The quality and quantity of tõlkeandmed significantly impact the performance of machine translation systems. Datasets that are clean, accurate, and representative of the domain for which translation is intended generally lead to better translation quality. Researchers and developers continuously work on curating, cleaning, and augmenting tõlkeandmed to push the boundaries of machine translation capabilities.