How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

Mackie, Iain; Dalton, Jeffery; Yates, Andrew

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_3347916_2

DetailsSummary

Released

Paper

How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

MPS-Authors

/persons/resource/persons206666

Yates, Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

arXiv:2105.07975.pdf
(Preprint), 905KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Mackie, I., Dalton, J., & Yates, A. (2021). How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset. Retrieved from https://arxiv.org/abs/2105.07975.

Cite as: https://hdl.handle.net/21.11116/0000-0009-67AB-3

Abstract

Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more
effectively evaluate neural ranking models on complex topics. It builds on TREC
Deep Learning (DL) topics by extensively annotating them with question intent
categories, answer types, wikified entities, topic categories, and result type
metadata from a commercial web search engine. Based on this data, we introduce
a framework for identifying challenging queries. DL-HARD contains fifty topics
from the official DL 2019/2020 evaluation benchmark, half of which are newly
and independently assessed. We perform experiments using the official submitted
runs to DL on DL-HARD and find substantial differences in metrics and the
ranking of participating systems. Overall, DL-HARD is a new resource that
promotes research on neural ranking methods by focusing on challenging and
complex topics.