CrossWord Puzzle 2

1. Across

Across

1. The process of automatically translating text or speech from one language to another using computer algorithms.
2. An MT approach that uses linguistic rules and bilingual dictionaries to translate text, focusing on syntax, morphology, and grammar.
3. An MT approach that uses statistical models based on bilingual text corpora to predict the probability of a translation.
4. An advanced MT approach that uses deep learning models, specifically neural networks, to translate text by analyzing large datasets and capturing context.
5. An MT approach that relies on a database of previously translated examples, finding the closest matches to translate new sentences.
6. A combination of different MT approaches, often integrating RBMT and SMT/NMT to leverage the strengths of each method.
7. Large collections of text in two languages, used to train and evaluate MT systems by providing parallel examples of translations.
8. A type of bilingual corpus where texts in two languages are aligned at the sentence level, facilitating the training of SMT and NMT systems.
9. A specific type of SMT that breaks down text into phrases rather than individual words, improving the fluency of translations.
10. A deep learning model used in NMT that processes sequences of text to generate translations, maintaining the order and context of words.
11. In SMT, a model that predicts the most likely translation of a word or phrase based on bilingual text data.
12. A model that assesses the fluency of the translated text by predicting the likelihood of word sequences in the target language.
13. In MT, the process that selects the best translation hypothesis based on the probabilities generated by the translation and language models.
14. A component in SMT that predicts the correct word order in the target language, addressing differences in syntax between languages.
15. Computational models inspired by the human brain, used in NMT to learn patterns and relationships in language data.
16. A technique in NMT that allows the model to focus on specific parts of the input sentence, improving translation accuracy, especially for long sentences.
17. A framework used in NMT where the encoder processes the input text and the decoder generates the translation, often using an attention mechanism.
18. A metric for evaluating the quality of machine-generated translations by comparing them to one or more reference translations.
19. In NMT, models that have been trained on large datasets and can be fine-tuned for specific tasks or languages, speeding up the development process.
20. The practice of applying knowledge gained from one task (e.g., translating English to French) to another related task (e.g., translating English to Spanish), commonly used in NMT.