Code and Datasets

Question answering NLU

Mahdi Namazifar, Alexandros Papangelis, Gokhan Tur, Dilek Hakkani-Tür

2021

Last updated January 25, 2024

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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.

This repository contains code to transform MATIS++ NLU data (e.g. utterances and intent / slot annotations) into SQuAD 2.0 format question-answering data that can be used by QANLU. MATIS++ includes the original English version of ATIS and a translation into eight languages: German, Spanish, French, Japanese, Hindi, Portuguese, Turkish, and Chinese.

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