Machine Translation Flashcards
7 cards from real NLP practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Translation flashcards as text
What is the primary purpose of the 'language model' component in a traditional statistical MT system?
Answer: Assigning probability to target-language word sequences to favor fluent output
The language model scores candidate translations by their fluency in the target language, preferring grammatically natural word sequences.
Which challenge is unique to translating morphologically rich languages (e.g., Finnish, Turkish) in NMT?
Answer: Data sparsity due to exponentially many word forms
Morphologically rich languages have vast inflectional paradigms, causing data sparsity since many word forms appear rarely or not at all in training data.
In constrained decoding for MT, what is a 'soft constraint' as opposed to a 'hard constraint'?
Answer: A preference that biases the model toward certain terms without guaranteeing their inclusion
Soft constraints adjust the probability distribution to favor particular terms but do not guarantee they appear, unlike hard constraints that force specific tokens.
What is 'adaptive MT' in a professional translation workflow?
Answer: A system that updates its model in real time based on translator corrections
Adaptive MT systems update their parameters on-the-fly as human translators post-edit output, personalizing the model to a specific user or domain during a session.
Which property of the transformer makes it more parallelizable during training compared to RNN-based MT models?
Answer: Self-attention operates on all positions simultaneously rather than sequentially
Transformer self-attention computes relationships between all token pairs in parallel, whereas RNNs must process tokens one at a time sequentially.
What is the role of 'length normalization' in beam search for MT?
Answer: Dividing the log-probability score by sentence length to avoid penalizing longer hypotheses
Without length normalization, beam search favors short translations because each additional token multiplies (reduces) the probability; dividing by length corrects this bias.
Which evaluation approach asks bilingual judges to rate MT output on adequacy and fluency separately?
Answer: Human evaluation using adequacy/fluency scales
The classic human MT evaluation protocol uses separate adequacy (meaning preserved?) and fluency (is target text natural?) rating scales judged by bilingual assessors.