Update app.py

app.py

@@ -79,6 +79,51 @@ elif menu == "Parsing NLU data into SQuAD 2.0":
             ]
         }
         ````
+        
+        The next step is to run the `atis.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu).
+        That script will produce a json file that looks like this:
+        
+        ````
+        {
+        "version": 1.0,
+        "data": [
+            {
+                "title": "MultiATIS++",
+                "paragraphs": [
+                    {
+                        "context": "yes. no. i am looking for some vietnamese food",
+                        "qas": [
+                            {
+                                "question": "did they ask for a restaurant?",
+                                "id": "49f1180cb9ce4178a8a90f76c21f69b4",
+                                "is_impossible": false,
+                                "answers": [
+                                    {
+                                        "text": "yes",
+                                        "answer_start": 0
+                                    }
+                                ],
+                                "slot": "",
+                                "intent": "restaurant"
+                            },
+                            {
+                                "question": "did they ask for music?",
+                                "id": "a7ffe039fb3e4843ae16d5a68194f45e",
+                                "is_impossible": false,
+                                "answers": [
+                                    {
+                                        "text": "no",
+                                        "answer_start": 5
+                                    }
+                                ],
+                                "slot": "",
+                                "intent": "restaurant"
+                            },
+                            ... <More questions>
+        ````
+        
+        There are many tunable parameters when generating the above file, such as how many negative examples to include per question. Follow the same process for training a slot-tagging model.
+        
     ''')