Natural Language Processing with Python Updated EditionChapter 171
Chapter 9: Machine Translation
Section 1 of 7-~ 1 min read-Synced from Cuantum content
1. What is the main purpose of a sequence-to-sequence model in machine translation? - A) To classify text into predefined categories
- B) To generate a summary of a text
- C) To translate text from one language to another
- D) To detect sentiment in a text
1. Which mechanism helps a sequence-to-sequence model focus on specific parts of the input sequence when generating the output sequence? - A) Tokenization
- B) Attention Mechanism
- C) Lemmatization
- D) Stemming
1. What is a primary advantage of using Transformer models over traditional RNNs for machine translation? - A) Reduced computational complexity
- B) Better handling of long-range dependencies
- C) Simpler model architecture
- D) Lower memory requirements