TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
This work supports researchers in information retrieval by facilitating the reuse of existing test collections, but it is incremental as it focuses on documentation and guidelines rather than novel methods or data.
The paper tackles the challenge of reusing TREC Deep Learning test collections for ad hoc search in the large data regime by documenting the datasets, providing best practices to avoid overfitting, and analyzing reusability issues, without presenting new experimental results or concrete numbers.
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that the best models with large data may be deep neural networks. This paper supports the reuse of the TREC DL test collections in three ways. First we describe the data sets in detail, documenting clearly and in one place some details that are otherwise scattered in track guidelines, overview papers and in our associated MS MARCO leaderboard pages. We intend this description to make it easy for newcomers to use the TREC DL data. Second, because there is some risk of iteration and selection bias when reusing a data set, we describe the best practices for writing a paper using TREC DL data, without overfitting. We provide some illustrative analysis. Finally we address a number of issues around the TREC DL data, including an analysis of reusability.