MLS-C01 Question #134: Real Exam Question with Answer & Explanation

Question

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long. Which action will resolve the problem?

Options

• AChange preprocessing to use n-grams.
• BAdd more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
• CAdjust hyperparameters related to the attention mechanism.
• DChoose a different weight initialization type.

Explanation

Attention mechanism. The disadvantage of an encoder-decoder framework is that model performance decreases as and when the length of the source sequence increases because of the limit of how much information the fixed-length encoded feature vector can contain. To tackle this problem, in 2015, Bahdanau et al. proposed the attention mechanism. In an attention mechanism, the decoder tries to find the location in the encoder sequence where the most important information could be located and uses that information and previously decoded words to predict the next token in the sequence. https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq-howitworks.html

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