|
--- |
|
language: en |
|
license: cc-by-4.0 |
|
widget: |
|
- context: "Yes. No. I'm looking for a cheap flight to Boston." |
|
- question: "Is the user looking to book a flight?" |
|
datasets: |
|
- atis |
|
--- |
|
|
|
# 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](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf). |
|
|
|
## 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](https://github.com/amazon-research/question-answering-nlu). |
|
|
|
## 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: |
|
|
|
```python |
|
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. |