Digital Humanities

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1. A method in NMT training where target language data is translated back into the source language to create additional training data, improving translation quality.
2. Smaller language components, such as prefixes or suffixes, used in NMT to handle rare or compound words more effectively.
3. The process of breaking down text into smaller units, such as words or subwords, to facilitate processing in MT systems.
4. The process of matching corresponding words or phrases between the source and target languages in a parallel corpus, crucial for training SMT and NMT systems.
5. Dense vector representations of words used in NMT to capture semantic meanings and relationships between words in different languages.
6. The process of fine-tuning an MT system to perform better in a specific domain, such as legal or medical translation.
7. The ability of an MT system to apply knowledge from one language pair to another, enhancing translation quality across multiple languages.
8. An NMT approach that handles multiple languages simultaneously, using a shared model that can translate between any pair of supported languages.
9. Languages that have limited digital resources, such as corpora or dictionaries, posing challenges for MT development.
10. Words that are not present in the training data of an MT system, often leading to translation errors.
11. The process of manually correcting errors in machine-generated translations to improve accuracy and fluency.
12. The process of analyzing the grammatical structure of sentences, used in RBMT to generate accurate translations.
13. The study of the structure of words and their components, such as roots and affixes, used in RBMT to handle inflected languages.
14. The process of determining the correct meaning of a word that has multiple possible interpretations, crucial in MT for accurate translations.
15. Identifying the roles played by words in a sentence, such as agent or object, to improve the accuracy of MT systems.
16. The combination of a source language and a target language in MT, such as English to Spanish.
17. An intermediate language used in MT when direct translation between two languages is difficult due to lack of resources.
18. Word embeddings that take into account the context in which a word appears, improving translation quality in NMT.
19. The process of artificially increasing the size of a training dataset by creating variations of existing data, used to improve MT performance.
20. A decoding algorithm used in NMT that considers multiple translation hypotheses simultaneously to find the most probable translation.
21. A regularization technique in NMT that prevents overfitting by randomly dropping units in the neural network during training.
22. The process of automatically finding and extracting parallel sentences from large bilingual corpora, used to improve the training of MT systems.
23. A term referring to the distinct linguistic patterns that emerge in machine-generated translations, often detectable by statistical analysis.
24. An MT approach where human translators interact with the MT system during the translation process, refining the output in real-time.
25. An approach that relies heavily on large text corpora for training MT systems, typical in SMT and NMT.
26. In SMT, a table that lists possible translations for phrases in the source language along with their probabilities.
27. A loss function used in NMT training to measure the difference between the predicted translation and the actual translation.
28. A type of language model used in NMT that predicts the next word in a sentence based on the context of previous words.
29. A technique in NMT where a smaller, simpler model is trained to replicate the behavior of a larger, more complex model, improving efficiency.
30. An MT approach that transfers linguistic structures from the source language to the target language, relying on syntactic and semantic transfer rules.