Update app.py

app.py

@@ -12,38 +12,47 @@ if menu == "Introduction":
 
     st.markdown('''
 
-Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, 
-leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of 
-training an intent classifier or a slot tagger, for example, we can ask the model intent- and 
-slot-related questions in natural language: 
-
-```
-Context : I'm looking for a cheap flight to Boston.
-
-Question: Is the user looking to book a flight?
-Answer  : Yes
-
-Question: Is the user asking about departure time?
-Answer  : No
-
-Question: What price is the user looking for?
-Answer  : cheap
-
-Question: Where is the user flying from?
-Answer  : (empty)
-```
-
-Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details,
-please read the paper: 
-[Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf).
-
-In this Space, we will see how to transform [MATIS++](https://github.com/amazon-research/multiatis) 
-NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
-question-answering data that can be used by QANLU. MATIS++ includes
-the original English version of ATIS and a translation into eight languages: German, Spanish, French, 
-Japanese, Hindi, Portuguese, Turkish, and Chinese. 
-
-''')
+        Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, 
+        leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of 
+        training an intent classifier or a slot tagger, for example, we can ask the model intent- and 
+        slot-related questions in natural language: 
+        
+        ```
+        Context : I'm looking for a cheap flight to Boston.
+        
+        Question: Is the user looking to book a flight?
+        Answer  : Yes
+        
+        Question: Is the user asking about departure time?
+        Answer  : No
+        
+        Question: What price is the user looking for?
+        Answer  : cheap
+        
+        Question: Where is the user flying from?
+        Answer  : (empty)
+        ```
+        
+        Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details,
+        please read the paper: 
+        [Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf).
+        
+        In this Space, we will see how to transform an example
+        NLU dataset (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
+        question-answering data that can be used by QANLU.
+
+    ''')
+
+elif menu == "Parsing NLU data into SQuAD 2.0":
+    st.markdown('''
+        Here, we show a small example of how NLU data can be transformed into QANLU data.
+        The same method can be used to transform [MATIS++](https://github.com/amazon-research/multiatis) 
+        NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
+        question-answering data that can be used by QANLU.
+        
+        Here is an example: 
+    ''')
+    
 
 elif menu == "Evaluation":
     st.header('QANLU Evaluation')