master: NMT model evaluation.

Author: purvasingh96

Chapter-wise code/Code - PyTorch/7. Attention Models/1. NMT/Evaluating NMT.md

@@ -1 +1,30 @@
-# Evaluating NMT Models
+# Evaluation for Machine Translation
+
+## BLEU Score
+
+### What is BLEU Score?
+
+1. The BLEU Score *(Bilingual Evaluation Understudy)* evaluates the quality of machine-translated text by comparing a candidate texts translation to one or more reference translations. <br><br>
+
+2.  The closer the BLEU score is to one, the better your model is. The closer to zero, the worse it is.
+
+3.To get a BLEU score, the candidates and the references are usually based on an average of uni, bi, try or even four-gram precision.<br> 
+<img src="./images/28. BLEU Score Calculation.png" width="40%"></img><br><br>
+
+### Disadvantages of BLEU
+
+1. BLEU doesn't consider the semantic meaning of words.
+2. BLUE doesn't consider the sentence structure of the sentence.
+
+## ROGUE Score
+
+### What is ROGUE Score?
+
+1. ROGUE stands for *Recall Oriented Understudy for Gisting Evaluation*, which is a mouthful. But let's you know right off the bat that it's more recall-oriented by default. This means that it's placing more importance on how much of the human created reference appears in the machine translation.
+2. It was orignially developed to evaluate text-summarization models but works well for NMT as well.
+3. ROGUE score calculates the precision and recall between the generated text and human-created text.
+<img src="./images/29. ROUGE Score Calculation.png" width="40%"></img><br><br>
+
+### Disadvantages of ROUGE
+
+1. Low ROUGUE score doesn't necessarily mean that translation is bad.