论文标题
TREC 2020深度学习曲目的构象与内核与查询术语独立
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
论文作者
论文摘要
我们在TREC 2020深度学习轨道的严格盲目评估设置下基准测试了构象代表模型。特别是，我们研究了合并的影响：（i）基于学习的表示形式（即“二重奏原理”）的明确术语匹配与补体匹配，（ii）查询术语独立性（即“ QTI假设”）以将模型扩展到完整的检索设置，以及（iii）单击数据，以及（iii）orcas单击数据作为其他文档描述字段。我们发现证据支持所有上述三种策略都可以改善检索质量。
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.
