Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
Microsoft（微软）；University College London（伦敦大学学院）；TU Wien（维也纳工业大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
英文摘要：
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the "Duet principle"), (ii) query term independence (i.e., the "QTI assumption") to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
