DAMO-NLP-SG/EQA-PMR-large

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}
}