Update app.py

app.py

@@ -6,5 +6,39 @@ st.sidebar.title('Navigation')
 menu = st.sidebar.radio("", options=["Introduction", "Parsing NLU data into SQuAD 2.0", "Generating Questions", "Training",
                                      "Evaluation"], index=0)
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.markdown('''
+
+## Question Answering NLU
+
+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. 
+
+''')