edit to assignment

NLP/3-Translation/README.md

@@ -105,4 +105,4 @@ Can you make Marvin even better by extracting other features from the user input
 
 There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/en-us/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Test some of the Pride and Prejudice sentences above and see if it can detect nuance.
 
-**Assignment**: [Try a different author](assignment.md)
+**Assignment**: [Poetic License](assignment.md)

NLP/3-Translation/assignment.md

@@ -1,8 +1,8 @@
-# Try a different author
+# Poetic license
 
 ## Instructions
 
-In [this notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency) you can find over 500 Emily Dickinson poems analyzed for sentiment using NLTK. Pick a poet or author of your choice and use these techniques, enhanced as you see fit, to determine their overall sentiment. Does anything surprise you?
+In [this notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency) you can find over 500 Emily Dickinson poems previously analyzed for sentiment using Azure text analytics. Using this dataset, analyze it using the techniques described in the lesson. Does the suggested sentiment of a poem match the more sophistic Azure service's decision? Why or why not, in your opinion? Does anything surprise you?
 ## Rubric
 
 | Criteria | Exemplary                                                                  | Adequate                                                | Needs Improvement        |