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.
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