readme: ---
  language: en
  tags:
  - PROP
  - Pretrain4IR
  - fill-mask
  
  license: apache-2.0
  datasets:
  - wikipedia
  ---
  
  
  # PROP-wiki
  
  **PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative of the “ideal” document. Based on this idea, we construct the representative words prediction (ROP) task for pre-training. The full paper can be found [here](https://arxiv.org/pdf/2010.10137.pdf).
  
  
  # Citation
  If you find our work useful, please consider citing our paper:
  ```bibtex
  @inproceedings{DBLP:conf/wsdm/MaGZFJC21,
    author    = {Xinyu Ma and
                 Jiafeng Guo and
                 Ruqing Zhang and
                 Yixing Fan and
                 Xiang Ji and
                 Xueqi Cheng},
    editor    = {Liane Lewin{-}Eytan and
                 David Carmel and
                 Elad Yom{-}Tov and
                 Eugene Agichtein and
                 Evgeniy Gabrilovich},
    title     = {{PROP:} Pre-training with Representative Words Prediction for Ad-hoc
                 Retrieval},
    booktitle = {{WSDM} '21, The Fourteenth {ACM} International Conference on Web Search
                 and Data Mining, Virtual Event, Israel, March 8-12, 2021},
    pages     = {283--291},
    publisher = {{ACM}},
    year      = {2021},
    url       = {https://doi.org/10.1145/3437963.3441777},
    doi       = {10.1145/3437963.3441777},
    timestamp = {Wed, 07 Apr 2021 16:17:44 +0200},
    biburl    = {https://dblp.org/rec/conf/wsdm/MaGZFJC21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```