
Ever wondered how computers know if a translation makes sense?
Image: The original uploader was Palmiped at English Wikipedia., CC BY 3.0, via Wikimedia Commons
Ever wondered how computers know if a translation makes sense?
Imagine you're using a translation app to understand a foreign movie's subtitles. You want to make sure the subtitles are accurate and readable.
Think of BLEU as a scorecard that compares the app's translation to a set of professional translations, giving you a sense of how good it is.
Example
If the app's translation closely matches the professional ones, it gets a high BLEU score, indicating better quality.
Remember this
BLEU score helps you quickly gauge the accuracy of machine translations.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Evaluation of machine translation
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BLEU measures precision of n-grams, ROUGE measures recall
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self-attention: Attention(Q,K,V) = softmax(QK^T/√d_k)V
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TF-IDF scoring
TF-IDF = (Term Frequency) * (Inverse Document Frequency)
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