Datasets › DL-HARD
DL-HARD (Deep Learning Hard)
Deep Learning Hard (DL-HARD) is an annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) questions extensively annotated with query intent categories, answer types, wikified entities, topic categories, and result type metadata from a leading web search engine.
DL-HARD contains 50 queries from the official 2019/2020 evaluation benchmark, half of which are newly and independently assessed. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex queries.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 9 papers for it but never published that list.
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
No task tagged in the archive.
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- DL-HARD
1 variant name, as the archive lists them.
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