MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders Are Better Dense Retrievers

Author:

Zhou KunORCID,Liu XiaoORCID,Gong YeyunORCID,Zhao Wayne XinORCID,Jiang DaxinORCID,Duan NanORCID,Wen Ji-RongORCID

Publisher

Springer Nature Switzerland

Reference47 articles.

1. Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the TREC 2020 deep learning track. arXiv preprint arXiv:2102.07662 (2021)

2. Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the TREC 2019 deep learning track. arXiv preprint arXiv:2003.07820 (2020)

3. Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of SIGIR 2019, pp. 985–988 (2019). https://doi.org/10.1145/3331184.3331303

4. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL 2019, pp. 4171–4186 (2019). https://aclanthology.org/N19-1423

5. Gao, L., Callan, J.: Condenser: a pre-training architecture for dense retrieval. In: Proceedings of EMNLP 2021, pp. 981–993 (2021). https://aclanthology.org/2021.emnlp-main.75

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