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@@ -6,7 +6,7 @@ training an intent classifier or a slot tagger, for example, we can ask the mode
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  slot-related questions in natural language:
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  ```
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- Context : I'm looking for a cheap flight to Boston.
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  Question: Is the user looking to book a flight?
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  Answer : Yes
@@ -21,6 +21,8 @@ Question: Where is the user flying from?
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  Answer : (empty)
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  ```
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  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).
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  ## Model training
 
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  slot-related questions in natural language:
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  ```
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+ Context : Yes. No. I'm looking for a cheap flight to Boston.
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  Question: Is the user looking to book a flight?
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  Answer : Yes
 
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  Answer : (empty)
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  ```
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+ Note the "Yes. No. " prepended in the context. Those are to allow the model to answer intent-related questions (`Is the user looking for a restaurant?`).
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+
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  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).
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  ## Model training