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The Deep Learning Track is a new track for TREC 2019, with the goal ofstudying ad hoc ranking in a large data regime. It is the first track withlarge human-labeled training sets, introducing two sets corresponding to twotasks, each with rigorous TREC-style blind evaluation and reusable test sets.The document retrieval task has a corpus of 3.2 million documents with 367thousand training queries, for which we generate a reusable test set of 43queries. The passage retrieval task has a corpus of 8.8 million passages with503 thousand training queries, for which we generate a reusable test set of 43queries. This year 15 groups submitted a total of 75 runs, using variouscombinations of deep learning, transfer learning and traditional IR rankingmethods. Deep learning runs significantly outperformed traditional IR runs.Possible explanations for this result are that we introduced large trainingdata 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.

Overview of the TREC 2019 deep learning track