Papers by Yingzhuo Qian
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)
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Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett
| Challenge: | Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available. |
| Approach: | They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. |
| Outcome: | The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains. |