Update README.md

README.md

@@ -7,7 +7,7 @@ It was introduced in the paper From Clozing to Comprehending: Retrofitting Pre-t
 Weiwen Xu, Xin Li, Wenxuan Zhang, Meng Zhou, Wai Lam, Luo Si, Lidong Bing
 and first released in [this repository](https://github.com/DAMO-NLP-SG/PMR).
 
-The model is initialized with roberta-base and further continued pre-trained with an MRC objective.
+This model is initialized with roberta-base and further continued pre-trained with an MRC objective.
 
 ## Model description
 The model is pre-trained with distantly labeled data using a learning objective called Wiki Anchor Extraction (WAE). 
@@ -43,23 +43,16 @@ There are three versions of models released. The details are:
 The models need to be fine-tuned on the data downstream tasks. During fine-tuning, no task-specific layer is required.
 
 ### How to use
-You can try the scripts from [this repo](https://github.com/DAMO-NLP-SG/PMR).
+You can try the codes from [this repo](https://github.com/DAMO-NLP-SG/PMR).
 
 
 
 ### BibTeX entry and citation info
 ```bibtxt
-@inproceedings{acl23/SSTuning,
-  author    = {Chaoqun Liu and
-               Wenxuan Zhang and
-               Guizhen Chen and
-               Xiaobao Wu and
-               Anh Tuan Luu and
-               Chip Hong Chang and 
-               Lidong Bing},
-  title     = {Zero-Shot Text Classification via Self-Supervised Tuning},
-  booktitle = {Findings of the 2023 ACL},
-  year      = {2023},
-  url       = {},
+@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}
 }
 ```