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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

  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},
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