Papers by Ryan Spring
Training Question Answering Models From Synthetic Data (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on question and answer generation aims to improve question answering models given limited amount of labeled data. |
| Approach: | They synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. |
| Outcome: | The proposed model achieves higher accuracy than the SQUAD1.1 training set questions using synthetic questions and answers than the training set question. |