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license: mit |
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This model is the **query** encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele. |
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ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules. |
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|NQ (Test)|R@5|R@20|R@20| |
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|:---|:---|:---|:---| |
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|ANCE-Tele|76.9|83.4|87.3| |
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``` |
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@inproceedings{sun2022ancetele, |
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title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives}, |
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author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao}, |
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booktitle={Proceedings of EMNLP 2022}, |
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year={2022} |
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} |
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``` |
