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Questions and Answers
What is the primary challenge faced by SMT?
What is the primary challenge faced by SMT?
- Complex sentence structures in source text
- Limited vocabulary in the target language
- Data redundancy in training sets
- Data sparsity in encountered words (correct)
Which of the following best describes data sparsity?
Which of the following best describes data sparsity?
- Rarity of words in the source text during training (correct)
- Consistency in word usage across various texts
- Frequent occurrence of words in training data
- Overlapping vocabulary in source and target texts
How does data sparsity impact SMT performance?
How does data sparsity impact SMT performance?
- It increases the speed of translation processes
- It enhances the accuracy of translations
- It reduces the complexity of translation algorithms
- It leads to ineffective handling of unknown words (correct)
Which factor contributes to data sparsity in training data?
Which factor contributes to data sparsity in training data?
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What problem arises when words in the source text are seldom seen in training?
What problem arises when words in the source text are seldom seen in training?
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Which scenario illustrates data sparsity in SMT?
Which scenario illustrates data sparsity in SMT?
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Study Notes
Fuzzy Matches
- Similar but not identical sentence matches are referred to as fuzzy matches.
- These matches are assessed on a scale ranging from 0% to 99% based on their similarity.
Machine Translation Approaches
- Rule-based Machine Translation (RBMT) focuses on linguistic rules and grammar.
- Statistics-based Machine Translation (SMT) relies on statistical models and data analysis.
Features of Machine Translation
- Sentential features in machine translation include tense, aspect, and modality.
- Analysis in RBMT prioritizes knowledge of the source language (SL) using generalizations about part-of-speech combinations.
Transfer Stage in RBMT
- A bilingual dictionary is used in the transfer stage to link SL structures with corresponding target language (TL) structures.
Challenges in SMT
- Data sparsity presents a major hurdle for SMT, particularly with rare or unseen words in the training data.
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