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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", "Generating Questions", "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?' | |
| ] | |
| } | |
| ```` | |
| ''') | |
| from atis import * | |
| 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) |