Update README.md

README.md

@@ -10,7 +10,7 @@ size_categories:
 # T2Ranking
 
 ## Introduction
-T2Ranking is a large-scale Chinese benchmark for passage ranking. The details about T2Ranking are elaborated in [this paper](https://github.com/THUIR/T2Ranking).
+T2Ranking is a large-scale Chinese benchmark for passage ranking. The details about T2Ranking are elaborated in [this paper](https://arxiv.org/abs/2304.03679#).
 
 
 Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking. 
@@ -21,7 +21,7 @@ To support the passage ranking research, various benchmark datasets are construc
 To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair. 
 
 
-<div align=center><img width="600" height="200" src="https://github.com/THUIR/T2Ranking/blob/main/pic/stat.png?raw=true"/></div>
+<div align=center><img width="600" height="200" src="pic/stat.png"/></div>
 <div align=center>Table 1: The data statistics of datasets commonly used in passage ranking. FR(SR): First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
 
 
@@ -107,14 +107,17 @@ python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data
 ## License
 The dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
 
+
 ## Citation
 If you use this dataset in your research, please cite our paper:
 ```
-@inproceedings{t2ranking,
-  title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
-  author={Xiaohui Xie, Qian Dong},
-  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
-  year={2023}
+@misc{xie2023t2ranking,
+      title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking}, 
+      author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
+      year={2023},
+      eprint={2304.03679},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
 }
 ```