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+# 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).
+
+To see how to train a QANLU model, visit the [Amazon Science repository](https://github.com/amazon-research/question-answering-nlu)
+
+## Use in transformers:
+
+'''
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+  
+tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True)
+
+model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True)
+'''
+
+## Citation
+If you use this work, please cite:
+
+```
+@inproceedings{namazifar2021language,
+  title={Language model is all you need: Natural language understanding as question answering},
+  author={Namazifar, Mahdi and Papangelis, Alexandros and Tur, Gokhan and Hakkani-T{\"u}r, Dilek},
+  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={7803--7807},
+  year={2021},
+  organization={IEEE}
+}
+```
+
+## License
+
+This library is licensed under the CC BY NC License.