论文标题
TREC 2019深度学习曲目的概述
Overview of the TREC 2019 deep learning track
论文作者
论文摘要
深度学习轨道是TREC 2019的新轨道，其目标是在大型数据制度中研究临时排名。这是第一个具有大型人体标记训练集的曲目，引入了两组，对应于两个任务，每个任务都具有严格的TREC风格的盲目评估和可重复使用的测试集。文档检索任务的语料库为320万个文档，其中包含36.7万次培训查询，为此我们生成了可重复使用的43个查询测试集。通道检索任务的语料库为880万通道，其中有5003,000个培训查询，为此我们生成了可重复使用的43个查询测试集。今年15个小组使用深度学习，转移学习和传统IR排名方法的各种组合进行了75次跑步。深度学习的运行极高地超过了传统的IR运行。对此结果的可能解释是，我们引入了大型培训数据，并在评审池中包括了对此类数据进行培训的深层模型，而过去的一些研究没有此类培训数据或汇总。
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC-style blind evaluation and reusable test sets. The document retrieval task has a corpus of 3.2 million documents with 367 thousand training queries, for which we generate a reusable test set of 43 queries. The passage retrieval task has a corpus of 8.8 million passages with 503 thousand training queries, for which we generate a reusable test set of 43 queries. This year 15 groups submitted a total of 75 runs, using various combinations of deep learning, transfer learning and traditional IR ranking methods. Deep learning runs significantly outperformed traditional IR runs. Possible explanations for this result are that we introduced large training data and we included deep models trained on such data in our judging pools, whereas some past studies did not have such training data or pooling.
