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jingtaozhan/README.md

Hi there 👋 This is Jingtao Zhan.

  • 🌱 I’m a third-year PhD student at Tsinghua IR Group supervised by Prof. Shaoping Ma and Prof. Yiqun Liu.
  • 🔭 My research lies in Information Retrieval and Web Search. I currently focus on Dense Retrieval with a wide interest in improving its effectiveness, efficiency, and interpretability. The publications are available at my homepage.
  • 📫 Contact me via jingtaozhan@gmail.com or twitter.

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  1. disentangled-retriever disentangled-retriever Public

    An easy-to-use python toolkit for flexibly adapting various neural ranking models to any target domain.

    Python 59 5

  2. RepCONC RepCONC Public

    WSDM'22 Best Paper: Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

    Python 118 13

  3. JPQ JPQ Public

    CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.

    Python 50 11

  4. DRhard DRhard Public

    SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.

    Python 127 14

  5. bert-ranking-analysis bert-ranking-analysis Public

    SIGIR'20: An Analysis of BERT in Document Ranking

    Python 22 4

  6. RepBERT-Index RepBERT-Index Public

    RepBERT is a competitive first-stage retrieval technique. It represents documents and queries with fixed-length contextualized embeddings. The inner products of them are regarded as relevance score…

    Python 66 9