Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2020 |
| Subtitle of host publication | Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 2967-2974 |
| Number of pages | 8 |
| ISBN (electronic) | 9781450368599 |
| Publication status | Published - 19 Oct 2020 |
| Externally published | Yes |
| Event | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - online, Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020 |
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.
Keywords
- machine learning, multilingual data, multimodal data, multitask learning
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Decision Sciences(all)
- General Decision Sciences
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
CIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery (ACM), 2020. p. 2967-2974.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - MLM
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Armitage, Jason
AU - Kacupaj, Endri
AU - Tahmasebzadeh, Golsa
AU - Swati,
AU - Maleshkova, Maria
AU - Ewerth, Ralph
AU - Lehmann, Jens
N1 - Funding information: MLM is supported by a team of researchers from the University of Bonn, the Leibniz Information Center for Science and Technology, and Jožef Stefan Institute. The resource is already in use for individual projects and as a contribution to the project deliverables of the Marie Sk?odowska-Curie CLEOPATRA Innovative Training Network. In addition to the steps above that make the resource available to the wider community, usage of MLM will be promoted to the network of researchers in this project. Awareness among researchers and practitioners in digital humanities will be promoted by demonstrations and presentations at domain-related events. The range of modalities and languages present in the dataset also extends its application to research on multimodal representation learning, multilingual machine learning, information retrieval, location estimation, and the Semantic Web. MLM will be supported and maintained for three years in the first instance. A second release of the dataset is already scheduled and the generation process outlined above is designed to enable rapid scaling. The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 812997.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - 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.
AB - 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.
KW - machine learning
KW - multilingual data
KW - multimodal data
KW - multitask learning
UR - http://www.scopus.com/inward/record.url?scp=85095865453&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2008.06376
DO - 10.48550/arXiv.2008.06376
M3 - Conference contribution
AN - SCOPUS:85095865453
SP - 2967
EP - 2974
BT - CIKM 2020
PB - Association for Computing Machinery (ACM)
Y2 - 19 October 2020 through 23 October 2020
ER -