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--- |
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language: en |
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license: cc-by-4.0 |
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widget: |
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- context: "Yes. No. I'm looking for a cheap flight to Boston." |
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datasets: |
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- atis |
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--- |
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# Question Answering NLU |
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Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, |
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leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of |
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training an intent classifier or a slot tagger, for example, we can ask the model intent- and |
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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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Question: Is the user asking about departure time? |
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Answer : No |
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Question: What price is the user looking for? |
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Answer : cheap |
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Question: Where is the user flying from? |
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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 (e.g. "Is the user looking for a restaurant?"). |
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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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Instructions for how to train and evaluate a QANLU model, as well as the necessary code for ATIS are in the [Amazon Science repository](https://github.com/amazon-research/question-answering-nlu). |
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## Intended use and limitations |
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This model has been fine-tuned on ATIS (English) and is intended to demonstrate the power of this approach. For other domains or tasks, it should be further fine-tuned |
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on relevant data. |
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## Use in transformers: |
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```python |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True) |
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model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True) |
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qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer) |
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qa_input = { |
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'context': 'Yes. No. I want a cheap flight to Boston.', |
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'question': 'What is the destination?' |
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} |
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answer = qa_pipeline(qa_input) |
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``` |
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## Citation |
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If you use this work, please cite: |
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``` |
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@inproceedings{namazifar2021language, |
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title={Language model is all you need: Natural language understanding as question answering}, |
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author={Namazifar, Mahdi and Papangelis, Alexandros and Tur, Gokhan and Hakkani-T{\"u}r, Dilek}, |
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booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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pages={7803--7807}, |
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year={2021}, |
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organization={IEEE} |
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} |
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``` |
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## License |
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This library is licensed under the CC BY NC License. |
