MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
Publication date
2020-10-19
Document type
Conference paper
Author
Organisational unit
Scopus ID
ISBN
Conference
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19 - 23, 2020
Series or journal
ACM Conferences
Book title
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Peer-reviewed
✅
Part of the university bibliography
Nein
Keyword
machine learning
multilingual data
multimodal data
multitask learning
Computer Science - Learning
Statistics - Machine Learning
Abstract
In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding.
Version
Not applicable (or unknown)
Access right on openHSU
Metadata only access