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import streamlit as st
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
st.title('Question-Answering NLU')
st.sidebar.title('Navigation')
menu = st.sidebar.radio("", options=["Introduction", "Parsing NLU data into SQuAD 2.0", "Training",
"Evaluation"], index=0)
if menu == "Introduction":
st.markdown('''
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).
In this Space, we will see how to transform an example
NLU dataset (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
question-answering data that can be used by QANLU.
''')
elif menu == "Parsing NLU data into SQuAD 2.0":
st.markdown('''
Here, we show a small example of how NLU data can be transformed into QANLU data.
The same method can be used to transform [MATIS++](https://github.com/amazon-research/multiatis)
NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
question-answering data that can be used by QANLU.
Here is an example dataset with three intents and two examples per intent:
````
restaurant, I am looking for some Vietnamese food
restaurant, What is there to eat around here?
music, Play my workout playlist
music, Can you find Bob Dylan songs?
flight, Show me flights from Oakland to Dallas
flight, I want two economy tickets from Miami to Chicago
````
Now, we need to define some questions, per intent. We can use free-form questions or use templates.
````
{
'restaurant': [
'Did they ask for a restaurant?',
'Did they mention a restaurant?'
],
'music': [
'Did they ask for music?',
'Do they want to play music?'
],
'flight': [
'Did they ask for a flight?',
'Do they want to book a flight?'
]
}
````
''')
elif menu == "Evaluation":
st.header('QANLU Evaluation')
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)
context = st.text_input(
'Please enter the context:',
value="I want a cheap flight to Boston."
)
question = st.text_input(
'Please enter the question:',
value="What is the destination?"
)
qa_input = {
'context': 'Yes. No. ' + context,
'question': question
}
if st.button('Ask QANLU'):
answer = qa_pipeline(qa_input)
st.write(answer) |