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 : Yes. No. 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)

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?").

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.

Model training

Instructions for how to train and evaluate a QANLU model, as well as the necessary code for ATIS are in the Amazon Science repository.

Intended use and limitations

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 on relevant data.

Use in transformers:

from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
  
tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True)

model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True)

qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)

qa_input = {
  'context': 'Yes. No. I want a cheap flight to Boston.',
  'question': 'What is the destination?'
}

answer = qa_pipeline(qa_input)

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.

Downloads last month
18
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Spaces using AmazonScience/qanlu 4