EQA-PMR-large
EQA-PMR-large is initialized with PMR-large and further fine-tuned on 6 Extractive Question Answering (EQA) training data from training split of MRQA.
The model performance on the in-dev sets are:
| SQuAD | NewsQA | HotpotQA | NaturalQuestions | TriviaQA | SearchQA | |
|---|---|---|---|---|---|---|
| RoBERTa-large (single-task model) | 94.2 | 73.8 | 81.6 | 83.3 | 85.1 | 85.7 |
| PMR-large (single-task model) | 94.5 | 74.0 | 83.6 | 83.8 | 85.1 | 88.3 |
| EQA-PMR-large (multi-task model) | 94.2 | 73.7 | 66.9 | 82.3 | 85.4 | 88.7 |
Note that the performance of RoBERTa-large and PMR-large are single-task fine-tuning, while EQA-PMR-large is a multi-task fine-tuned model. As it is fine-tuned on multiple datasets, we believe that EQA-PMR-large has a better generalization capability to other EQA tasks than PMR-large and RoBERTa-large.
How to use
You can try the codes from this repo for both training and inference.
BibTeX entry and citation info
@article{xu2022clozing,
title={From Clozing to Comprehending: Retrofitting Pre-trained Language Model to Pre-trained Machine Reader},
author={Xu, Weiwen and Li, Xin and Zhang, Wenxuan and Zhou, Meng and Bing, Lidong and Lam, Wai and Si, Luo},
journal={arXiv preprint arXiv:2212.04755},
year={2022}
}
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