Unsupervised Question Answering
How to train a model to answer questions when you have no annotated data
Table of Contents
Introduction
Generating the questions
1. Cloze Generation
- Obtaining the context
- Defining the answers
- Obtaining cloze statements
2. Translating into natural questions
- Identity mapping
- Noisy clozes
- Unsupervised Neural Machine Translation (UNMT)
Training the QA model
1. The XLNet model
2. Results
Introduction
Question Answering
Question Answering models do exactly what the name suggests: given a paragraph of text and a question, the model looks for the answer in the paragraph. A subfield of Question Answering called Reading Comprehension is a rapidly progressing domain of Natural Language Processing. Indeed, several models have already surpassed human performance on the Stanford Question Answering Dataset (SQuAD).
