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

Mackie, I., Dalton, J. and Yates, A. (2021) How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset. In: SIGIR 2021: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 11-15 July 2021, pp. 2335-2341. (doi:10.1145/3404835.3463262)

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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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mackie, Iain and Dalton, Dr Jeff
Authors: Mackie, I., Dalton, J., and Yates, A.
College/School:College of Science and Engineering
College of Science and Engineering > School of Computing Science
Published Online:11 July 2021
Copyright Holders:Copyright © 2021 Copyright held by the owner/author(s)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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